title stringlengths 5 342 | author stringlengths 3 2.17k | year int64 1.95k 2.02k | abstract stringlengths 0 12.7k | pages stringlengths 1 702 | queryID stringlengths 4 40 | query stringlengths 1 300 | paperID stringlengths 0 40 | include int64 0 1 |
|---|---|---|---|---|---|---|---|---|
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks | Jin, Hailong and
Hou, Lei and
Li, Juanzi and
Dong, Tiansi | 2,019 | This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We c... | 4969--4978 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 074e3497b03366caf2e17acd59fb1c52ccf8be55 | 1 |
{EUSP}: An Easy-to-Use Semantic Parsing {P}lat{F}orm | An, Bo and
Bo, Chen and
Han, Xianpei and
Sun, Le | 2,019 | Semantic parsing aims to map natural language utterances into structured meaning representations. We present a modular platform, EUSP (Easy-to-Use Semantic Parsing PlatForm), that facilitates developers to build semantic parser from scratch. Instead of requiring a large amount of training data or complex grammar knowle... | 67--72 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 0aa3aa92f19aaaeeb02444a4ed7995de2ce643e3 | 0 |
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | Li, Bangzheng and
Yin, Wenpeng and
Chen, Muhao | 2,022 | The task of ultra-fine entity typing (UFET) seeks to predict diverse and free-form words or phrases that describe the appropriate types of entities mentioned in sentences. A key challenge for this task lies in the large number of types and the scarcity of annotated data per type. Existing systems formulate the task as ... | 607--622 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | 1 |
{AB}/{BA} analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy | Petegrosso, Raphael and
Baderdinnni, VasistaKrishna and
Senechal, Thibaud and
Bullough, Benjamin | 2,022 | Evaluation of keyword spotting (KWS) systems that detect keywords in speech is a challenging task under realistic privacy constraints. The KWS is designed to only collect data when the keyword is present, limiting the availability of hard samples that may contain false negatives, and preventing direct estimation of mod... | 27--36 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 62348960bd30f562ef733261b1a47b6d1981f8cd | 0 |
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss | Xu, Peng and
Barbosa, Denilson | 2,018 | The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt... | 16--25 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 008405f7ee96677ac23cc38be360832af2d9f437 | 1 |
Strategies and Challenges for Crowdsourcing Regional Dialect Perception Data for {S}wiss {G}erman and {S}wiss {F}rench | Goldman, Jean-Philippe and
Clematide, Simon and
Avanzi, Mathieu and
Tandler, Raphael | 2,018 | nan | nan | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 47829603b26c306c68242cdde6200fa6aa4d9083 | 0 |
Label Semantic Aware Pre-training for Few-shot Text Classification | Mueller, Aaron and
Krone, Jason and
Romeo, Salvatore and
Mansour, Saab and
Mansimov, Elman and
Zhang, Yi and
Roth, Dan | 2,022 | In text classification tasks, useful information is encoded in the label names. Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction. However, use of label-semantics during pre-training has not been extensively explored. We therefore... | 8318--8334 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 17ae9c4297e0feb23b2ef84a406d76dc7033c98c | 1 |
{ELRC} Action: Covering Confidentiality, Correctness and Cross-linguality | Vanallemeersch, Tom and
Defauw, Arne and
Szoc, Sara and
Kramchaninova, Alina and
Van den Bogaert, Joachim and
L{\"o}sch, Andrea | 2,022 | We describe the language technology (LT) assessments carried out in the ELRC action (European Language Resource Coordination) of the European Commission, which aims towards minimising language barriers across the EU. We zoom in on the two most extensive assessments. These LT specifications do not only involve experimen... | 6240--6249 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | c35ea7cbf1571e6dc30afcaf4368dbb87df295ff | 0 |
Fine-grained Entity Typing via Label Reasoning | Liu, Qing and
Lin, Hongyu and
Xiao, Xinyan and
Han, Xianpei and
Sun, Le and
Wu, Hua | 2,021 | Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowle... | 4611--4622 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 7f30821267a11138497107d947ea39726e4b7fbd | 1 |
{COVID}-Fact: Fact Extraction and Verification of Real-World Claims on {COVID}-19 Pandemic | Saakyan, Arkadiy and
Chakrabarty, Tuhin and
Muresan, Smaranda | 2,021 | We introduce a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claim... | 2116--2129 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | c530bef97ee809c01ce59d04a7011d445fb1e147 | 0 |
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model | Dai, Hongliang and
Song, Yangqiu and
Wang, Haixun | 2,021 | Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions. A key challenge for this ultra-fine entity typing task is that human annotated data are extremely scarce,... | 1790--1799 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 70b49a024787d3ad374fb78dc87e3ba2b5e16566 | 1 |
A Fine-Grained Domain Adaption Model for Joint Word Segmentation and {POS} Tagging | Jiang, Peijie and
Long, Dingkun and
Sun, Yueheng and
Zhang, Meishan and
Xu, Guangwei and
Xie, Pengjun | 2,021 | Domain adaption for word segmentation and POS tagging is a challenging problem for Chinese lexical processing. Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain. Previous work usually assumes a universal source-to-target ad... | 3587--3598 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | ce29e0aa6e6569f137d1d248ec497a63c65235fe | 0 |
Modeling Fine-Grained Entity Types with Box Embeddings | Onoe, Yasumasa and
Boratko, Michael and
McCallum, Andrew and
Durrett, Greg | 2,021 | Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types{'} complex interdependencies. We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchie... | 2051--2064 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 176e3cbe3141c8b874df663711dca9b7470b8243 | 1 |
Which is Better for Deep Learning: Python or {MATLAB}? Answering Comparative Questions in Natural Language | Chekalina, Viktoriia and
Bondarenko, Alexander and
Biemann, Chris and
Beloucif, Meriem and
Logacheva, Varvara and
Panchenko, Alexander | 2,021 | We present a system for answering comparative questions (Is X better than Y with respect to Z?) in natural language. Answering such questions is important for assisting humans in making informed decisions. The key component of our system is a natural language interface for comparative QA that can be used in personal as... | 302--311 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 45d9d0d9b74605135e3e0dfe3b84661952013760 | 0 |
Few-{NERD}: A Few-shot Named Entity Recognition Dataset | Ding, Ning and
Xu, Guangwei and
Chen, Yulin and
Wang, Xiaobin and
Han, Xu and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan | 2,021 | Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empiric... | 3198--3213 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | a293a01ddd639b25360cf4f23e2df8dd0d1caa8e | 1 |
Weakly Supervised Pre-Training for Multi-Hop Retriever | Seonwoo, Yeon and
Lee, Sang-Woo and
Kim, Ji-Hoon and
Ha, Jung-Woo and
Oh, Alice | 2,021 | nan | 694--704 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 9651c3f83b9310829622305f5316443253861fba | 0 |
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification | L{\'o}pez, Federico and
Strube, Michael | 2,020 | Label inventories for fine-grained entity typing have grown in size and complexity. Nonetheless, they exhibit a hierarchical structure. Hyperbolic spaces offer a mathematically appealing approach for learning hierarchical representations of symbolic data. However, it is not clear how to integrate hyperbolic components ... | 460--475 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 9109814833731812513bb80f99c94277fc459625 | 1 |
Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations | Yu, Jianfei and
Jiang, Jing and
Khoo, Ling Min Serena and
Chieu, Hai Leong and
Xia, Rui | 2,020 | The prevalent use of social media enables rapid spread of rumors on a massive scale, which leads to the emerging need of automatic rumor verification (RV). A number of previous studies focus on leveraging stance classification to enhance RV with multi-task learning (MTL) methods. However, most of these methods failed t... | 1392--1401 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 65bd80bc3498f7feef170da29ceb58fea28f652b | 0 |
An Investigation of Potential Function Designs for Neural {CRF} | Hu, Zechuan and
Jiang, Yong and
Bach, Nguyen and
Wang, Tao and
Huang, Zhongqiang and
Huang, Fei and
Tu, Kewei | 2,020 | The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling. In this paper, we investigate a series of increasingly expressive potential functions for neural CRF models, which not only integrate the emission and transition functions, but also explicitly take the representations of the... | 2600--2609 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | d587e3b402064be3c0321e4bf88cc598893e6439 | 1 |
Human-Paraphrased References Improve Neural Machine Translation | Freitag, Markus and
Foster, George and
Grangier, David and
Cherry, Colin | 2,020 | Automatic evaluation comparing candidate translations to human-generated paraphrases of reference translations has recently been proposed by freitag2020bleu. When used in place of original references, the paraphrased versions produce metric scores that correlate better with human judgment. This effect holds for a varie... | 1183--1192 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | d8fe41ef4f202e01aac9d78a589e22734cea8e07 | 0 |
Learning to Denoise Distantly-Labeled Data for Entity Typing | Onoe, Yasumasa and
Durrett, Greg | 2,019 | Distantly-labeled data can be used to scale up training of statistical models, but it is typically noisy and that noise can vary with the distant labeling technique. In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and de... | 2407--2417 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | dc138300b87f5bfccec609644d5edc08c4d783e9 | 1 |
{P}ro{S}eqo: Projection Sequence Networks for On-Device Text Classification | Kozareva, Zornitsa and
Ravi, Sujith | 2,019 | We propose a novel on-device sequence model for text classification using recurrent projections. Our model ProSeqo uses dynamic recurrent projections without the need to store or look up any pre-trained embeddings. This results in fast and compact neural networks that can perform on-device inference for complex short a... | 3894--3903 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | cc26c8dae566a7aed07179db77c1cc0d5ca427db | 0 |
Ultra-Fine Entity Typing | Choi, Eunsol and
Levy, Omer and
Choi, Yejin and
Zettlemoyer, Luke | 2,018 | We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head wor... | 87--96 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 4157834ed2d2fea6b6f652a72a9d0487edbc9f57 | 1 |
Semantic role labeling tools for biomedical question answering: a study of selected tools on the {B}io{ASQ} datasets | Eckert, Fabian and
Neves, Mariana | 2,018 | Question answering (QA) systems usually rely on advanced natural language processing components to precisely understand the questions and extract the answers. Semantic role labeling (SRL) is known to boost performance for QA, but its use for biomedical texts has not yet been fully studied. We analyzed the performance o... | 11--21 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | e8d5e16e2302fccdda6730fa9f1600d8c1419431 | 0 |
{O}nto{N}otes: The 90{\%} Solution | Hovy, Eduard and
Marcus, Mitchell and
Palmer, Martha and
Ramshaw, Lance and
Weischedel, Ralph | 2,006 | nan | 57--60 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | e54d8b07ef659f9ee2671441c4355e414e408836 | 1 |
Compiling a Lexicon of Cooking Actions for Animation Generation | Shirai, Kiyoaki and
Ookawa, Hiroshi | 2,006 | nan | 771--778 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 8235935ce1d7e58d45fa63f114bdc98a91746ecb | 0 |
Improving Semantic Parsing via Answer Type Inference | Yavuz, Semih and
Gur, Izzeddin and
Su, Yu and
Srivatsa, Mudhakar and
Yan, Xifeng | 2,016 | nan | 149--159 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | f3594f9d60c98cac88f9033c69c2b666713ed6d6 | 1 |
Potential impact of {QT}21 | Cornelius, Eleanor | 2,016 | nan | nan | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 90a4a2036ef89350722752c4ed657d55d83aa2ba | 0 |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing | Xiong, Wenhan and
Wu, Jiawei and
Lei, Deren and
Yu, Mo and
Chang, Shiyu and
Guo, Xiaoxiao and
Wang, William Yang | 2,019 | Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such techniques, however, are not directly applicable to more open and practical scenarios where the type set is not restricted by KB schema and ... | 773--784 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | a0713d945b2e5c2bdeeba68399c8ac6ea84e0ca6 | 1 |
A Qualitative Comparison of {C}o{QA}, {SQ}u{AD} 2.0 and {Q}u{AC} | Yatskar, Mark | 2,019 | We compare three new datasets for question answering: SQuAD 2.0, QuAC, and CoQA, along several of their new features: (1) unanswerable questions, (2) multi-turn interactions, and (3) abstractive answers. We show that the datasets provide complementary coverage of the first two aspects, but weak coverage of the third. B... | 2318--2323 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 0a5606f0d56c618aa610cb1677e2788a3bd678fa | 0 |
{ZS}-{BERT}: Towards Zero-Shot Relation Extraction with Attribute Representation Learning | Chen, Chih-Yao and
Li, Cheng-Te | 2,021 | While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage. In this paper, we formulate the zero-shot relation extraction problem by in... | 3470--3479 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 93df9dc530b1cf0af6d5eef90d017741a2aab5d8 | 1 |
Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs | Cai, Jianyu and
Zhang, Zhanqiu and
Wu, Feng and
Wang, Jie | 2,021 | nan | 219--229 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 14ee04939eae5610d5d6141ad953021967ab2de5 | 0 |
Prompt-learning for Fine-grained Entity Typing | Ding, Ning and
Chen, Yulin and
Han, Xu and
Xu, Guangwei and
Wang, Xiaobin and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan and
Li, Juanzi and
Kim, Hong-Gee | 2,022 | As an effective approach to adapting pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using cloze-style language prompts to stimulate the versatile knowledge of PLMs, prompt-learning can achieve promising results on a series of NLP tasks, ... | 6888--6901 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | bf722dc893ddaad5045fca5646212ec3badf3c5a | 1 |
{M}emo{S}en: A Multimodal Dataset for Sentiment Analysis of Memes | Hossain, Eftekhar and
Sharif, Omar and
Hoque, Mohammed Moshiul | 2,022 | Posting and sharing memes have become a powerful expedient of expressing opinions on social media in recent days. Analysis of sentiment from memes has gained much attention to researchers due to its substantial implications in various domains like finance and politics. Past studies on sentiment analysis of memes have p... | 1542--1554 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | fc34892a8419d5378c456d70d54494920320bd55 | 0 |
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks | Jin, Hailong and
Hou, Lei and
Li, Juanzi and
Dong, Tiansi | 2,019 | This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We c... | 4969--4978 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 074e3497b03366caf2e17acd59fb1c52ccf8be55 | 1 |
Automatic Data-Driven Approaches for Evaluating the Phonemic Verbal Fluency Task with Healthy Adults | Lindsay, Hali and
Linz, Nicklas and
Troeger, Johannes and
Alexandersson, Jan | 2,019 | nan | 17--24 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | efd1a3ad8a0803e34d073380eefdf5381a2dfaf5 | 0 |
{L}earning from {C}ontext or {N}ames? {A}n {E}mpirical {S}tudy on {N}eural {R}elation {E}xtraction | Peng, Hao and
Gao, Tianyu and
Han, Xu and
Lin, Yankai and
Li, Peng and
Liu, Zhiyuan and
Sun, Maosong and
Zhou, Jie | 2,020 | Neural models have achieved remarkable success on relation extraction (RE) benchmarks. However, there is no clear understanding what information in text affects existing RE models to make decisions and how to further improve the performance of these models. To this end, we empirically study the effect of two main infor... | 3661--3672 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 6a5608e6fee3ecc65361525906b0d092ad9952bb | 1 |
Unknown Intent Detection Using {G}aussian Mixture Model with an Application to Zero-shot Intent Classification | Yan, Guangfeng and
Fan, Lu and
Li, Qimai and
Liu, Han and
Zhang, Xiaotong and
Wu, Xiao-Ming and
Lam, Albert Y.S. | 2,020 | User intent classification plays a vital role in dialogue systems. Since user intent may frequently change over time in many realistic scenarios, unknown (new) intent detection has become an essential problem, where the study has just begun. This paper proposes a semantic-enhanced Gaussian mixture model (SEG) for unkno... | 1050--1060 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | b387b19b8c02f3087bacd8514ea31e55e494ccf7 | 0 |
{CLEVE}: {C}ontrastive {P}re-training for {E}vent {E}xtraction | Wang, Ziqi and
Wang, Xiaozhi and
Han, Xu and
Lin, Yankai and
Hou, Lei and
Liu, Zhiyuan and
Li, Peng and
Li, Juanzi and
Zhou, Jie | 2,021 | Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot take full advantage of large-scale unsupervised data. To this end, we propose CLEV... | 6283--6297 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 2580aed3ac10d971f86d21f4c06db2de0cfb3c22 | 1 |
Epistemic Semantics in Guarded String Models | Campbell, Eric Hayden and
Rooth, Mats | 2,021 | nan | 81--90 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 57666117528e99ae907bcfb67b080d700cf83ece | 0 |
An Improved Baseline for Sentence-level Relation Extraction | Zhou, Wenxuan and
Chen, Muhao | 2,022 | Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. Many efforts have been devoted to this problem, while the best performing methods are still far from perfect. In this paper, we revisit two problems that affect the performance of existing RE models, namely ... | 161--168 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 11baa9cc02d6158edd9cb1f299579dad7828e162 | 1 |
Raccoons at {S}em{E}val-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition | Dogra, Atharvan and
Kaur, Prabsimran and
Kohli, Guneet and
Bedi, Jatin | 2,022 | Named Entity Recognition (NER), an essential subtask in NLP that identifies text belonging to predefined semantics such as a person, location, organization, drug, time, clinical procedure, biological protein, etc. NER plays a vital role in various fields such as informationextraction, question answering, and machine tr... | 1576--1582 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 6b8c47ca2fcd98e2e1a876cc02c60a5b2183b381 | 0 |
Improving Fine-grained Entity Typing with Entity Linking | Dai, Hongliang and
Du, Donghong and
Li, Xin and
Song, Yangqiu | 2,019 | Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this paper, we use entity linking to help with the fine-grained entity type classification process. We propose a deep neural model that makes predi... | 6210--6215 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | b74b272c7fe881614f3eb8c2504b037439571eec | 1 |
{S}amvaadhana: A {T}elugu Dialogue System in Hospital Domain | Duggenpudi, Suma Reddy and
Siva Subrahamanyam Varma, Kusampudi and
Mamidi, Radhika | 2,019 | In this paper, a dialogue system for Hospital domain in Telugu, which is a resource-poor Dravidian language, has been built. It handles various hospital and doctor related queries. The main aim of this paper is to present an approach for modelling a dialogue system in a resource-poor language by combining linguistic an... | 234--242 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | a51ac7085dc35cc9c4c8fffab2f4821fadc7af9e | 0 |
Event Detection with Multi-Order Graph Convolution and Aggregated Attention | Yan, Haoran and
Jin, Xiaolong and
Meng, Xiangbin and
Guo, Jiafeng and
Cheng, Xueqi | 2,019 | Syntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and related entities than sentence representations. But, existing studies only use first-order syntactic relation... | 5766--5770 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | d9213d53aeb3ee0a2a5db9024f8d75afd8c6f4d7 | 1 |
Two Discourse Tree - Based Approaches to Indexing Answers | Galitsky, Boris and
Ilvovsky, Dmitry | 2,019 | We explore anatomy of answers with respect to which text fragments from an answer are worth matching with a question and which should not be matched. We apply the Rhetorical Structure Theory to build a discourse tree of an answer and select elementary discourse units that are suitable for indexing. Manual rules for sel... | 367--372 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | a0924f96924980ef1f414c169d00da1ecfb98b5b | 0 |
Adversarial Training for Weakly Supervised Event Detection | Wang, Xiaozhi and
Han, Xu and
Liu, Zhiyuan and
Sun, Maosong and
Li, Peng | 2,019 | Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled data. However, these methods typically rely on sophisticated pre-defined rules as well as existing instances in knowledge bases for automatic annotation and thus su... | 998--1008 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 569829ab76a311b1f7f5b33d37ffff6a3fae6490 | 1 |
A Comparison of Sense-level Sentiment Scores | Bond, Francis and
Janz, Arkadiusz and
Piasecki, Maciej | 2,019 | In this paper, we compare a variety of sense-tagged sentiment resources, including SentiWordNet, ML-Senticon, plWordNet emo and the NTU Multilingual Corpus. The goal is to investigate the quality of the resources and see how well the sentiment polarity annotation maps across languages. | 363--372 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | eb44b5c7b75a32786a1bc025dc1f8304dd4d3444 | 0 |
Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction | Sainz, Oscar and
Lopez de Lacalle, Oier and
Labaka, Gorka and
Barrena, Ander and
Agirre, Eneko | 2,021 | Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment e... | 1199--1212 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 85061c524fdd5ec75f06a3329352621bb8d05f43 | 1 |
L{'}identification de langue, un outil au service du corse et de l{'}{\'e}valuation des ressources linguistiques [Language identification, a tool for {C}orsican and for the evaluation of linguistic resources] | Kevers, Laurent | 2,021 | nan | 13--37 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 0a8058b64a5f708fa2ec7d6f7d1ed26ae57cc331 | 0 |
Exploring the zero-shot limit of {F}ew{R}el | Cetoli, Alberto | 2,020 | This paper proposes a general purpose relation extractor that uses Wikidata descriptions to represent the relation{'}s surface form. The results are tested on the FewRel 1.0 dataset, which provides an excellent framework for training and evaluating the proposed zero-shot learning system in English. This relation extrac... | 1447--1451 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | c41fee04146a7893948b978676f0fc19fa622f94 | 1 |
Large Corpus of {C}zech Parliament Plenary Hearings | Kratochvil, Jon{\'a}{\v{s}} and
Pol{\'a}k, Peter and
Bojar, Ond{\v{r}}ej | 2,020 | We present a large corpus of Czech parliament plenary sessions. The corpus consists of approximately 1200 hours of speech data and corresponding text transcriptions. The whole corpus has been segmented to short audio segments making it suitable for both training and evaluation of automatic speech recognition (ASR) syst... | 6363--6367 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | c990a60c9f0004a74222aa2e84dcd2b2f238fa0d | 0 |
Zero-shot User Intent Detection via Capsule Neural Networks | Xia, Congying and
Zhang, Chenwei and
Yan, Xiaohui and
Chang, Yi and
Yu, Philip | 2,018 | User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users{'} utterances as intents are diversely expre... | 3090--3099 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 17e61004345661deef6ee9b749c54b6a5a8c76ac | 1 |
Exploring Conversational Language Generation for Rich Content about Hotels | Walker, Marilyn and
Smither, Albry and
Oraby, Shereen and
Harrison, Vrindavan and
Shemtov, Hadar | 2,018 | nan | nan | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 1a4d8740decca98ac41b7ec7de97172de8bcff77 | 0 |
Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification | Chen, Haibin and
Ma, Qianli and
Lin, Zhenxi and
Yan, Jiangyue | 2,021 | Hierarchical text classification is an important yet challenging task due to the complex structure of the label hierarchy. Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of the hierarchical information. To this end, we formulate the text-label semantics relations... | 4370--4379 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 57516ba4a5356154b81a9332010544dce24ee494 | 1 |
{SNACS} Annotation of Case Markers and Adpositions in {H}indi | Arora, Aryaman and
Venkateswaran, Nitin and
Schneider, Nathan | 2,021 | nan | 454--458 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 764dc286a4183adc19f49614386177cb999f0144 | 0 |
{T}axo{C}lass: Hierarchical Multi-Label Text Classification Using Only Class Names | Shen, Jiaming and
Qiu, Wenda and
Meng, Yu and
Shang, Jingbo and
Ren, Xiang and
Han, Jiawei | 2,021 | Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of classes from a taxonomic class hierarchy. Most existing HMTC methods train classifiers using massive human-labeled documents, which are often too costly to obtain in real-world applications. In this paper, we explore to conduct ... | 4239--4249 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 15e100120f080b9ef4230b4cbb8e107b76e2b839 | 1 |
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering | Karamcheti, Siddharth and
Krishna, Ranjay and
Fei-Fei, Li and
Manning, Christopher | 2,021 | Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datas... | 7265--7281 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 5441598e2b690a15198b7a38359e5936e4a46114 | 0 |
Relation Classification with Entity Type Restriction | Lyu, Shengfei and
Chen, Huanhuan | 2,021 | nan | 390--395 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 97cbc8a78ad588931d7adfe319b4c68f3d167461 | 1 |
Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering | Wang, Zhiguo and
Ng, Patrick and
Nallapati, Ramesh and
Xiang, Bing | 2,021 | Question answering over knowledge bases (KBQA) usually involves three sub-tasks, namely topic entity detection, entity linking and relation detection. Due to the large number of entities and relations inside knowledge bases (KB), previous work usually utilized sophisticated rules to narrow down the search space and man... | 347--357 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 489efd419d5690bdf8a255a9de8458e320f306c2 | 0 |
{M}ap{RE}: An Effective Semantic Mapping Approach for Low-resource Relation Extraction | Dong, Manqing and
Pan, Chunguang and
Luo, Zhipeng | 2,021 | Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works try leveraging the advance in few-shot learning to solve the low resource problem, where they train label-agnostic models to directly compare ... | 2694--2704 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 35f26a3c3f9013e419b47c928d92c333a0e09aa3 | 1 |
Auditing Keyword Queries Over Text Documents | Apparreddy, Bharath Kumar Reddy and
Rajanala, Sailaja and
Singh, Manish | 2,021 | Data security and privacy is an issue of growing importance in the healthcare domain. In this paper, we present an auditing system to detect privacy violations for unstructured text documents such as healthcare records. Given a sensitive document, we present an anomaly detection algorithm that can find the top-k suspic... | 378--387 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 046abb93061df136f6aace440ebd13e22d8a272c | 0 |
Fine-grained Entity Typing via Label Reasoning | Liu, Qing and
Lin, Hongyu and
Xiao, Xinyan and
Han, Xianpei and
Sun, Le and
Wu, Hua | 2,021 | Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowle... | 4611--4622 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 7f30821267a11138497107d947ea39726e4b7fbd | 1 |
{M}i{SS}@{WMT}21: Contrastive Learning-reinforced Domain Adaptation in Neural Machine Translation | Li, Zuchao and
Utiyama, Masao and
Sumita, Eiichiro and
Zhao, Hai | 2,021 | In this paper, we describe our MiSS system that participated in the WMT21 news translation task. We mainly participated in the evaluation of the three translation directions of English-Chinese and Japanese-English translation tasks. In the systems submitted, we primarily considered wider networks, deeper networks, rela... | 154--161 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 79159305945229a16f1c02bde93e8015ebd7dc55 | 0 |
Learning from Noisy Labels for Entity-Centric Information Extraction | Zhou, Wenxuan and
Chen, Muhao | 2,021 | Recent information extraction approaches have relied on training deep neural models. However, such models can easily overfit noisy labels and suffer from performance degradation. While it is very costly to filter noisy labels in large learning resources, recent studies show that such labels take more training steps to ... | 5381--5392 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | dbfc17833434243e07c4629e58f3d8ed7112dbfe | 1 |
{SHAPELURN}: An Interactive Language Learning Game with Logical Inference | Stein, Katharina and
Harter, Leonie and
Geiger, Luisa | 2,021 | We investigate if a model can learn natural language with minimal linguistic input through interaction. Addressing this question, we design and implement an interactive language learning game that learns logical semantic representations compositionally. Our game allows us to explore the benefits of logical inference fo... | 16--24 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 67f3d4addcfb9066ec436934f8d48ac58fa2b479 | 0 |
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model | Dai, Hongliang and
Song, Yangqiu and
Wang, Haixun | 2,021 | Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions. A key challenge for this ultra-fine entity typing task is that human annotated data are extremely scarce,... | 1790--1799 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 70b49a024787d3ad374fb78dc87e3ba2b5e16566 | 1 |
Euphemistic Phrase Detection by Masked Language Model | Zhu, Wanzheng and
Bhat, Suma | 2,021 | It is a well-known approach for fringe groups and organizations to use euphemisms{---}ordinary-sounding and innocent-looking words with a secret meaning{---}to conceal what they are discussing. For instance, drug dealers often use {``}pot{''} for marijuana and {``}avocado{''} for heroin. From a social media content mod... | 163--168 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 8bcb8dd3fadb35320fad382abda725e49454be6f | 0 |
Modeling Fine-Grained Entity Types with Box Embeddings | Onoe, Yasumasa and
Boratko, Michael and
McCallum, Andrew and
Durrett, Greg | 2,021 | Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types{'} complex interdependencies. We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchie... | 2051--2064 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 176e3cbe3141c8b874df663711dca9b7470b8243 | 1 |
Auditing Keyword Queries Over Text Documents | Apparreddy, Bharath Kumar Reddy and
Rajanala, Sailaja and
Singh, Manish | 2,021 | Data security and privacy is an issue of growing importance in the healthcare domain. In this paper, we present an auditing system to detect privacy violations for unstructured text documents such as healthcare records. Given a sensitive document, we present an anomaly detection algorithm that can find the top-k suspic... | 378--387 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 046abb93061df136f6aace440ebd13e22d8a272c | 0 |
Interpretable Entity Representations through Large-Scale Typing | Onoe, Yasumasa and
Durrett, Greg | 2,020 | In standard methodology for natural language processing, entities in text are typically embedded in dense vector spaces with pre-trained models. The embeddings produced this way are effective when fed into downstream models, but they require end-task fine-tuning and are fundamentally difficult to interpret. In this pap... | 612--624 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 782a50a48ba5d32839631254285d989bfadfd193 | 1 |
Understanding Pure Character-Based Neural Machine Translation: The Case of Translating {F}innish into {E}nglish | Tang, Gongbo and
Sennrich, Rico and
Nivre, Joakim | 2,020 | Recent work has shown that deeper character-based neural machine translation (NMT) models can outperform subword-based models. However, it is still unclear what makes deeper character-based models successful. In this paper, we conduct an investigation into pure character-based models in the case of translating Finnish ... | 4251--4262 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 48df86003af463a518ebee931cbc6114fe45651a | 0 |
{MAVEN}: {A} {M}assive {G}eneral {D}omain {E}vent {D}etection {D}ataset | Wang, Xiaozhi and
Wang, Ziqi and
Han, Xu and
Jiang, Wangyi and
Han, Rong and
Liu, Zhiyuan and
Li, Juanzi and
Li, Peng and
Lin, Yankai and
Zhou, Jie | 2,020 | Event detection (ED), which means identifying event trigger words and classifying event types, is the first and most fundamental step for extracting event knowledge from plain text. Most existing datasets exhibit the following issues that limit further development of ED: (1) Data scarcity. Existing small-scale datasets... | 1652--1671 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 46e84b0a7b3761d1a3c1577c66225453ab2cbc1c | 1 |
Interpreting Neural {CWI} Classifiers{'} Weights as Vocabulary Size | Ehara, Yo | 2,020 | Complex Word Identification (CWI) is a task for the identification of words that are challenging for second-language learners to read. Even though the use of neural classifiers is now common in CWI, the interpretation of their parameters remains difficult. This paper analyzes neural CWI classifiers and shows that some ... | 171--176 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 8cba5bf43132b3f7785e2bdde80aef26e38fa9d4 | 0 |
Improving {AMR} Parsing with Sequence-to-Sequence Pre-training | Xu, Dongqin and
Li, Junhui and
Zhu, Muhua and
Zhang, Min and
Zhou, Guodong | 2,020 | In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance. To alleviate such data size restriction, pre-trained models have been drawing more and more attention in AMR parsing. H... | 2501--2511 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 12b28c2d1b58234daa0f06ab43353c401eda1958 | 1 |
{F}lex{E}val, cr{\'e}ation de sites web l{\'e}gers pour des campagnes de tests perceptifs multim{\'e}dias ({F}lex{E}val, creation of light websites for multimedia perceptual test campaigns) | Fayet, C{\'e}dric and
Blond, Alexis and
Coulombel, Gr{\'e}goire and
Simon, Claude and
Lolive, Damien and
Lecorv{\'e}, Gw{\'e}nol{\'e} and
Chevelu, Jonathan and
Le Maguer, S{\'e}bastien | 2,020 | Nous pr{\'e}sentons FlexEval, un outil de conception et d{\'e}ploiement de tests perceptifs multim{\'e}dias sous la forme d{'}un site web l{\'e}ger. S{'}appuyant sur des technologies standards et ouvertes du web, notamment le framework Flask, FlexEval offre une grande souplesse de conception, des gages de p{\'e}rennit{... | 22--25 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 29bbf9d73cfc35ec9e6e602968eb0f76bba0fc91 | 0 |
What Are You Trying to Do? Semantic Typing of Event Processes | Chen, Muhao and
Zhang, Hongming and
Wang, Haoyu and
Roth, Dan | 2,020 | This paper studies a new cognitively motivated semantic typing task,multi-axis event process typing, that, given anevent process, attempts to infer free-form typelabels describing (i) the type of action made bythe process and (ii) the type of object the pro-cess seeks to affect. This task is inspired bycomputational an... | 531--542 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 7a2206c883165864e545f25cf00259e29eec058f | 1 |
Modularized Syntactic Neural Networks for Sentence Classification | Wu, Haiyan and
Liu, Ying and
Shi, Shaoyun | 2,020 | This paper focuses on tree-based modeling for the sentence classification task. In existing works, aggregating on a syntax tree usually considers local information of sub-trees. In contrast, in addition to the local information, our proposed Modularized Syntactic Neural Network (MSNN) utilizes the syntax category label... | 2786--2792 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 9f609d4ccebe4d651515375d3481bbcd5fe963f9 | 0 |
Entity-Relation Extraction as Multi-Turn Question Answering | Li, Xiaoya and
Yin, Fan and
Sun, Zijun and
Li, Xiayu and
Yuan, Arianna and
Chai, Duo and
Zhou, Mingxin and
Li, Jiwei | 2,019 | In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and elations is transformed to the task of identifying answer spans from the context. This multi-turn QA formalization comes with several key... | 1340--1350 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 2c5ec74fb56fbfbceaa4cd5c8312ada4e2e19503 | 1 |
Identifying Grammar Rules for Language Education with Dependency Parsing in {G}erman | Metheniti, Eleni and
Park, Pomi and
Kolesova, Kristina and
Neumann, G{\"u}nter | 2,019 | nan | 100--111 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | dea084fbd77e6a8dd14f03edba06eca633ca0964 | 0 |
Learning to Denoise Distantly-Labeled Data for Entity Typing | Onoe, Yasumasa and
Durrett, Greg | 2,019 | Distantly-labeled data can be used to scale up training of statistical models, but it is typically noisy and that noise can vary with the distant labeling technique. In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and de... | 2407--2417 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | dc138300b87f5bfccec609644d5edc08c4d783e9 | 1 |
{HABL}ex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation | Thompson, Brian and
Knowles, Rebecca and
Zhang, Xuan and
Khayrallah, Huda and
Duh, Kevin and
Koehn, Philipp | 2,019 | Bilingual lexicons are valuable resources used by professional human translators. While these resources can be easily incorporated in statistical machine translation, it is unclear how to best do so in the neural framework. In this work, we present the HABLex dataset, designed to test methods for bilingual lexicon inte... | 1382--1387 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 5d9bb7e6fa899ec8e1de66389cfeb5639044c56b | 0 |
Ultra-Fine Entity Typing | Choi, Eunsol and
Levy, Omer and
Choi, Yejin and
Zettlemoyer, Luke | 2,018 | We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head wor... | 87--96 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | 4157834ed2d2fea6b6f652a72a9d0487edbc9f57 | 1 |
Training for Diversity in Image Paragraph Captioning | Melas-Kyriazi, Luke and
Rush, Alexander and
Han, George | 2,018 | Image paragraph captioning models aim to produce detailed descriptions of a source image. These models use similar techniques as standard image captioning models, but they have encountered issues in text generation, notably a lack of diversity between sentences, that have limited their effectiveness. In this work, we c... | 757--761 | 8ba4a5f890b13b1cee77cdc976a712245cc6e9c0 | Unified Semantic Typing with Meaningful Label Inference | b8298cf0056af5afa3185181ddd5f6bb03181696 | 0 |
Improving Semantic Parsing via Answer Type Inference | Yavuz, Semih and
Gur, Izzeddin and
Su, Yu and
Srivatsa, Mudhakar and
Yan, Xifeng | 2,016 | nan | 149--159 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | f3594f9d60c98cac88f9033c69c2b666713ed6d6 | 1 |
What{'}s the Issue Here?: Task-based Evaluation of Reader Comment Summarization Systems | Barker, Emma and
Paramita, Monica and
Funk, Adam and
Kurtic, Emina and
Aker, Ahmet and
Foster, Jonathan and
Hepple, Mark and
Gaizauskas, Robert | 2,016 | Automatic summarization of reader comments in on-line news is an extremely challenging task and a capability for which there is a clear need. Work to date has focussed on producing extractive summaries using well-known techniques imported from other areas of language processing. But are extractive summaries of comments... | 3094--3101 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | e7c7d867a4729953f500db6f8dbd5266b04af9b9 | 0 |
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks | Das, Rajarshi and
Zaheer, Manzil and
Reddy, Siva and
McCallum, Andrew | 2,017 | Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the ... | 358--365 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 2b2090eab4abe27e6e5e4ca94afaf82e511b63bd | 1 |
Personalized Machine Translation: Preserving Original Author Traits | Rabinovich, Ella and
Patel, Raj Nath and
Mirkin, Shachar and
Specia, Lucia and
Wintner, Shuly | 2,017 | The language that we produce reflects our personality, and various personal and demographic characteristics can be detected in natural language texts. We focus on one particular personal trait of the author, gender, and study how it is manifested in original texts and in translations. We show that author{'}s gender has... | 1074--1084 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 07fab9a1a5d8e65ce50965f514f2d0e6022a6b94 | 0 |
Mapping Text to Knowledge Graph Entities using Multi-Sense {LSTM}s | Kartsaklis, Dimitri and
Pilehvar, Mohammad Taher and
Collier, Nigel | 2,018 | This paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph. The compositional model is an LSTM equipped with a dynamic disambig... | 1959--1970 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 33b60f5493e1a1cb303dd33569925e0ed0c7e189 | 1 |
Multi-Source Multi-Class Fake News Detection | Karimi, Hamid and
Roy, Proteek and
Saba-Sadiya, Sari and
Tang, Jiliang | 2,018 | Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years. Fake news is typically written intentionally to mislead readers, which determines that fake news detection merely based on news content is treme... | 1546--1557 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 03aa69b71705890cb1555effddbd91ade9aa234c | 0 |
Bidirectional {LSTM}-{CRF} for Named Entity Recognition | Panchendrarajan, Rrubaa and
Amaresan, Aravindh | 2,018 | nan | nan | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 574453271bf9dbce7df005e9e1c2e0bb77eb1c6d | 1 |
{IPSL}: A Database of Iconicity Patterns in Sign Languages. Creation and Use | Kimmelman, Vadim and
Klezovich, Anna and
Moroz, George | 2,018 | nan | nan | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 9950b5aaeb9b73554568b0630ef490f5457d110e | 0 |
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering | Xiong, Wenhan and
Wang, Hong and
Wang, William Yang | 2,021 | Commonly used information retrieval methods such as TF-IDF in open-domain question answering (QA) systems are insufficient to capture deep semantic matching that goes beyond lexical overlaps. Some recent studies consider the retrieval process as maximum inner product search (MIPS) using dense question and paragraph rep... | 2803--2815 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 469d92f195aebfa09e9b411ad92b3c879bcd1eba | 1 |
Coreference-Aware Dialogue Summarization | Liu, Zhengyuan and
Shi, Ke and
Chen, Nancy | 2,021 | Summarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions. Examples of such challenges include unstructured information exchange in dialogues, informal interactions between speakers, and dynamic role changes of speakers as the di... | 509--519 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 4696d6dfaf78ce2a65c3111550a50eff9423b896 | 0 |
Proceedings of the Workshop Computational Semantics Beyond Events and Roles | nan | 2,017 | nan | nan | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 04482d34aacdd6d0170d0935855ee5b403b84aa9 | 1 |
Morphology-based Entity and Relational Entity Extraction Framework for {A}rabic | Jaber, Amin and
Zaraket, Fadi A. | 2,017 | nan | 97--121 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 46a6d8578f3f61965992039df8a4c8aabdff275f | 0 |
Question Answering on {F}reebase via Relation Extraction and Textual Evidence | Xu, Kun and
Reddy, Siva and
Feng, Yansong and
Huang, Songfang and
Zhao, Dongyan | 2,016 | nan | 2326--2336 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | e3919e94c811fd85f5038926fa354619861674f9 | 1 |
{DTS}im at {S}em{E}val-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics | Banjade, Rajendra and
Maharjan, Nabin and
Gautam, Dipesh and
Rus, Vasile | 2,016 | nan | 640--644 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 60ace2c8f672f56702257296918a26e1c99c3862 | 0 |
Constraint-Based Question Answering with Knowledge Graph | Bao, Junwei and
Duan, Nan and
Yan, Zhao and
Zhou, Ming and
Zhao, Tiejun | 2,016 | WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work. Most questions in them are {`}simple{'} questions which can be answered based on a single relation in the knowledge base. Such data-sets lack the capability of evaluating KBQA systems on ... | 2503--2514 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | 3689102f44116a46304ec512594478a1c615ae02 | 1 |
Improving Statistical Machine Translation Performance by Oracle-{BLEU} Model Re-estimation | Dakwale, Praveen and
Monz, Christof | 2,016 | nan | 38--44 | 0ce715758e4a7d62bfb1c4cebcca8afa520694f3 | Schema and content aware classification for predicting the sources containing an answer over corpus and knowledge graphs | fc452d9d926e14bca793e44c3ee8f8760521852e | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.