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id stringlengths 40 40	pid stringlengths 42 42	input stringlengths 8.37k 169k	output stringlengths 1 1.63k
66f0dee89f084fe0565539a73f5bbe65f3677814	66f0dee89f084fe0565539a73f5bbe65f3677814_0	Q: Do they use pretrained word representations in their neural network models? Text: Introduction Grammatical error correction (GEC) is a challenging task due to the variability of the type of errors and the syntactic and semantic dependencies of the errors on the surrounding context. Most of the grammatical error corr...	No
8f882f414d7ea12077930451ae77c6e5f093adbc	8f882f414d7ea12077930451ae77c6e5f093adbc_0	Q: How do they combine the two proposed neural network models? Text: Introduction Grammatical error correction (GEC) is a challenging task due to the variability of the type of errors and the syntactic and semantic dependencies of the errors on the surrounding context. Most of the grammatical error correction systems u...	ncorporating NNGLM and NNJM both independently and jointly into, baseline system
a49832c89a2d7f95c1fe6132902d74e4e7a3f2d0	a49832c89a2d7f95c1fe6132902d74e4e7a3f2d0_0	Q: Which dataset do they evaluate grammatical error correction on? Text: Introduction Grammatical error correction (GEC) is a challenging task due to the variability of the type of errors and the syntactic and semantic dependencies of the errors on the surrounding context. Most of the grammatical error correction syste...	CoNLL 2014
a33ab5ce8497ff63ca575a80b03e0ed9c6acd273	a33ab5ce8497ff63ca575a80b03e0ed9c6acd273_0	Q: How many users/clicks does their search engine have? Text: Introduction Over the past few years, major commercial search engines have enriched and improved the user experience by proactively presenting related entities for a query along with the regular web search results. Figure FIGREF3 shows an example of Alibaba ...	Unanswerable
8fcbae7c3bd85034ae074fa58a35e773936edb5b	8fcbae7c3bd85034ae074fa58a35e773936edb5b_0	Q: what was their baseline comparison? Text: Introduction Time-critical analysis of social media data streams is important for many application areas. For instance, responders to humanitarian disasters (e.g., earthquake, flood) need information about the disasters to determine what help is needed and where. This inform...	Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF)
cbbcafffda7107358fa5bf02409a01e17ee56bfd	cbbcafffda7107358fa5bf02409a01e17ee56bfd_0	Q: Was any variation in results observed based on language typology? Text: Introduction Neural networks are the backbone of modern state-of-the-art Natural Language Processing (NLP) systems. One inherent by-product of training a neural network is the production of real-valued representations. Many speculate that these ...	It is observed some variability - but not significant. Bert does not seem to gain much more syntax information than with type level information.
1e59263f7aa7dd5acb53c8749f627cf68683adee	1e59263f7aa7dd5acb53c8749f627cf68683adee_0	Q: Does the work explicitly study the relationship between model complexity and linguistic structure encoding? Text: Introduction Neural networks are the backbone of modern state-of-the-art Natural Language Processing (NLP) systems. One inherent by-product of training a neural network is the production of real-valued r...	No
eac042734f76e787cb98ba3d0c13a916a49bdfb3	eac042734f76e787cb98ba3d0c13a916a49bdfb3_0	Q: Which datasets are used in this work? Text: Introduction Among the several senses that The Oxford English Dictionary, the most venerable dictionary of English, provides for the word event, are the following. Although an event may refer to anything that happens, we are usually interested in occurrences that are of so...	GENIA corpus
9595bf228c9e859b0dc745e6c74070be2468d2cf	9595bf228c9e859b0dc745e6c74070be2468d2cf_0	Q: Does the training dataset provide logical form supervision? Text: Introduction Open domain semantic parsing aims to map natural language utterances to structured meaning representations. Recently, seq2seq based approaches have achieved promising performance by structure-aware networks, such as sequence-to-actionBIBR...	Yes
94c5f5b1eb8414ad924c3568cedd81dc35f29c48	94c5f5b1eb8414ad924c3568cedd81dc35f29c48_0	Q: What is the difference between the full test set and the hard test set? Text: Introduction Open domain semantic parsing aims to map natural language utterances to structured meaning representations. Recently, seq2seq based approaches have achieved promising performance by structure-aware networks, such as sequence-t...	3000 hard samples are selected from the test set
ba05a53f5563b9dd51cc2db241c6e9418bc00031	ba05a53f5563b9dd51cc2db241c6e9418bc00031_0	Q: How is the discriminative training formulation different from the standard ones? Text: Introduction The cocktail party problem BIBREF0 , BIBREF1 , referring to multi-talker overlapped speech recognition, is critical to enable automatic speech recognition (ASR) scenarios such as automatic meeting transcription, autom...	the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$
7bf3a7d19f17cf01f2c9fa16401ef04a3bef65d8	7bf3a7d19f17cf01f2c9fa16401ef04a3bef65d8_0	Q: How are the two datasets artificially overlapped? Text: Introduction The cocktail party problem BIBREF0 , BIBREF1 , referring to multi-talker overlapped speech recognition, is critical to enable automatic speech recognition (ASR) scenarios such as automatic meeting transcription, automatic captioning for audio/video...	we sort the speech segments by length, we take segments in pairs, zero-padding the shorter segment so both have the same length, These pairs are then mixed together
20f7b359f09c37e6aaaa15c2cdbb52b031ab4809	20f7b359f09c37e6aaaa15c2cdbb52b031ab4809_0	Q: What baseline system is used? Text: Introduction Mining Twitter data has increasingly been attracting much research attention in many NLP applications such as in sentiment analysis BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and stock market prediction BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 ....	Unanswerable
3efc0981e7f959d916aa8bb32ab1c347b8474ff8	3efc0981e7f959d916aa8bb32ab1c347b8474ff8_0	Q: What type of lexical, syntactic, semantic and polarity features are used? Text: Introduction Mining Twitter data has increasingly been attracting much research attention in many NLP applications such as in sentiment analysis BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and stock market prediction BIBREF...	Our lexical features include 1-, 2-, and 3-grams in both word and character levels., number of characters and the number of words, POS tags, 300-dimensional pre-trained word embeddings from GloVe, latent semantic indexing, tweet representation by applying the Brown clustering algorithm, positive words (e.g., love), neg...
10f560fe8e1c0c7dea5e308ee4cec16d07874f1d	10f560fe8e1c0c7dea5e308ee4cec16d07874f1d_0	Q: How does nextsum work? Text: Introduction Writing a summary is a different task compared to producing a longer article. As a consequence, it is likely that the topic and discourse moves made in summaries differ from those in regular articles. In this work, we present a powerful extractive summarization system which ...	selects the next summary sentence based not only on properties of the source text, but also on the previously selected sentences in the summary
07580f78b04554eea9bb6d3a1fc7ca0d37d5c612	07580f78b04554eea9bb6d3a1fc7ca0d37d5c612_0	Q: Can the approach be generalized to other technical domains as well? Text: Introduction Neural machine translation (NMT), a new approach to solving machine translation, has achieved promising results BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . An NMT system builds a simple large neural netw...	There is no reason to think that this approach wouldn't also be successful for other technical domains. Technical terms are replaced with tokens, therefore so as long as there is a corresponding process for identifying and replacing technical terms in the new domain this approach could be viable.
dc28ac845602904c2522f5349374153f378c42d3	dc28ac845602904c2522f5349374153f378c42d3_0	Q: How many tweets were manually labelled? Text: Abstract Currency trading (Forex) is the largest world market in terms of volume. We analyze trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classification mod...	44,000 tweets
ac148fb921cce9c8e7b559bba36e54b63ef86350	ac148fb921cce9c8e7b559bba36e54b63ef86350_0	Q: What dataset they use for evaluation? Text: Introduction Machine summarization systems have made significant progress in recent years, especially in the domain of news text. This has been made possible among other things by the popularization of the neural sequence-to-sequence (seq2seq) paradigm BIBREF0, BIBREF1, BI...	The same 2K set from Gigaword used in BIBREF7
094ce2f912aa3ced9eb97b171745d38f58f946dd	094ce2f912aa3ced9eb97b171745d38f58f946dd_0	Q: What is the source of the tables? Text: INTRODUCTION Today most of business-related information is transmitted in an electronic form, such as emails. Therefore, converting these messages into an easily analyzable representation could open numerous business opportunities, as a lot of them are not used fully because o...	The Online Retail Data Set consists of a clean list of 25873 invoices, totaling 541909 rows and 8 columns.
b5bfa6effdeae8ee864d7d11bc5f3e1766171c2d	b5bfa6effdeae8ee864d7d11bc5f3e1766171c2d_0	Q: Which regions of the United States do they consider? Text: Introduction Human language reflects cultural, political, and social evolution. Words are the atoms of language. Their meanings and usage patterns reveal insight into the dynamical process by which society changes. Indeed, the increasing frequency with which...	all regions except those that are colored black
bf00808353eec22b4801c922cce7b1ec0ff3b777	bf00808353eec22b4801c922cce7b1ec0ff3b777_0	Q: Why did they only consider six years of published books? Text: Introduction Human language reflects cultural, political, and social evolution. Words are the atoms of language. Their meanings and usage patterns reveal insight into the dynamical process by which society changes. Indeed, the increasing frequency with w...	Unanswerable
ec62c4cdbeaafc875c695f2d4415bce285015763	ec62c4cdbeaafc875c695f2d4415bce285015763_0	Q: What state-of-the-art general-purpose pretrained models are made available under the unified API? Text: Introduction In the past 18 months, advances on many Natural Language Processing (NLP) tasks have been dominated by deep learning models and, more specifically, the use of Transfer Learning methods BIBREF0 in whi...	BERT, RoBERTa, DistilBERT, GPT, GPT2, Transformer-XL, XLNet, XLM
405964517f372629cda4326d8efadde0206b7751	405964517f372629cda4326d8efadde0206b7751_0	Q: How is performance measured? Text: Introduction For the past 20 years, topic models have been used as a means of dimension reduction on text data, in order to ascertain underlying themes, or `topics', from documents. These probabilistic models have frequently been applied to machine learning problems, such as web sp...	they use ROC curves and cross-validation
ae95a7d286cb7a0d5bc1a8283ecbf803e9305951	ae95a7d286cb7a0d5bc1a8283ecbf803e9305951_0	Q: What models are included in the toolkit? Text: Introduction Being one of the prominent natural language generation tasks, neural abstractive text summarization (NATS) has gained a lot of popularity BIBREF0 , BIBREF1 , BIBREF2 . Different from extractive text summarization BIBREF3 , BIBREF4 , BIBREF5 , NATS relies on...	recurrent neural network (RNN)-based sequence-to-sequence (Seq2Seq) models for NATS
0be0c8106df5fde4b544af766ec3d4a3d7a6c8a2	0be0c8106df5fde4b544af766ec3d4a3d7a6c8a2_0	Q: Is there any human evaluation involved in evaluating this famework? Text: Introduction In this work, we aim to develop an automatic Language-Based Image Editing (LBIE) system. Given a source image, which can be a sketch, a grayscale image or a natural image, the system will automatically generate a target image by e...	Yes
959490ba72bd02f742db1e7b19525d4b6c419772	959490ba72bd02f742db1e7b19525d4b6c419772_0	Q: How big is multilingual dataset? Text: Introduction The MULTEXT-East project, (Multilingual Text Tools and Corpora for Central and Eastern European Languages) ran from ’95 to ’97 and developed standardised language resources for six Central and Eastern European languages, as well as for English, the “hub” language o...	Unanswerable
d76ecdc0743893a895bc9dc3772af47d325e6d07	d76ecdc0743893a895bc9dc3772af47d325e6d07_0	Q: How big are datasets for 2019 Amazon Alexa competition? Text: Vision Prompt: What is your team’s vision for your Socialbot? How do you want your customers to feel at the end of an interaction with your socialbot? How would your team measure success in competition? Our vision is made up of the following main points: ...	Unanswerable
2a6469f8f6bf16577b590732d30266fd2486a72e	2a6469f8f6bf16577b590732d30266fd2486a72e_0	Q: What is novel in author's approach? Text: Vision Prompt: What is your team’s vision for your Socialbot? How do you want your customers to feel at the end of an interaction with your socialbot? How would your team measure success in competition? Our vision is made up of the following main points: 1. A natural, engagi...	They use self-play learning , optimize the model for specific metrics, train separate models per user, use model and response classification predictors, and filter the dataset to obtain higher quality training data.
78577fd1c09c0766f6e7d625196adcc72ddc8438	78577fd1c09c0766f6e7d625196adcc72ddc8438_0	Q: What dataset is used for train/test of this method? Text: Introduction Corresponding author email: tshubhi@amazon.com. Paper submitted to IEEE ICASSP 2020 Recent advances in TTS have improved the achievable synthetic speech naturalness to near human-like capabilities BIBREF0, BIBREF1, BIBREF2, BIBREF3. This means th...	Training datasets: TTS System dataset and embedding selection dataset. Evaluation datasets: Common Prosody Errors dataset and LFR dataset.
1f63ccc379f01ecdccaa02ed0912970610c84b72	1f63ccc379f01ecdccaa02ed0912970610c84b72_0	Q: How much is the gap between using the proposed objective and using only cross-entropy objective? Text: Introduction Existing state-of-the-art question answering models are trained to produce exact answer spans for a question and a document. In this setting, a ground truth answer used to supervise the model is define...	The mixed objective improves EM by 2.5% and F1 by 2.2%
736c74d2f61ac8d3ac31c45c6510a36c767a5d6d	736c74d2f61ac8d3ac31c45c6510a36c767a5d6d_0	Q: What is the multi-instance learning? Text: Introduction A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and paired texts BIBREF0 , BIBREF1 , BIBREF2 . These correspondences describe how data representations are e...	Unanswerable
b2254f9dd0e416ee37b577cef75ffa36cbcb8293	b2254f9dd0e416ee37b577cef75ffa36cbcb8293_0	Q: How many domains of ontologies do they gather data from? Text: Introduction Within the field of ontology engineering, Competency Questions (CQs) BIBREF0 are natural language questions outlining the scope of knowledge represented by an ontology. They represent functional requirements in the sense that the developed o...	5 domains: software, stuff, african wildlife, healthcare, datatypes
cb1126992a39555e154bedec388465b249a02ded	cb1126992a39555e154bedec388465b249a02ded_0	Q: How is the semi-structured knowledge base created? Text: Introduction Answering questions posed in natural language is a fundamental AI task, with a large number of impressive QA systems built over the years. Today's Internet search engines, for instance, can successfully retrieve factoid style answers to many natur...	using a mixture of manual and semi-automatic techniques
d5256d684b5f1b1ec648d996c358e66fe51f4904	d5256d684b5f1b1ec648d996c358e66fe51f4904_0	Q: what is the practical application for this paper? Text: Introduction Morphology deals with the internal structure of words BIBREF0 , BIBREF1 . Languages of the world have different word production processes. Morphological richness vary from language to language, depending on their linguistic typology. In natural lan...	Improve existing NLP methods. Improve linguistic analysis. Measure impact of word normalization tools.
2a1069ae3629ae8ecc19d2305f23445c0231dc39	2a1069ae3629ae8ecc19d2305f23445c0231dc39_0	Q: Do they use a neural model for their task? Text: Introduction The notion of word sense is central to computational lexical semantics. Word senses can be either encoded manually in lexical resources or induced automatically from text. The former knowledge-based sense representations, such as those found in the BabelN...	No
0b411f942c6e2e34e3d81cc855332f815b6bc123	0b411f942c6e2e34e3d81cc855332f815b6bc123_0	Q: What's the method used here? Text: Introduction The task of automatic text summarization aims to compress a textual document to a shorter highlight while keeping salient information of the original text. In general, there are two ways to do text summarization: Extractive and Abstractive BIBREF0. Extractive approache...	Two neural networks: an extractor based on an encoder (BERT) and a decoder (LSTM Pointer Network BIBREF22) and an abstractor identical to the one proposed in BIBREF8.
01123a39574bdc4684aafa59c52d956b532d2e53	01123a39574bdc4684aafa59c52d956b532d2e53_0	Q: By how much does their method outperform state-of-the-art OOD detection? Text: Introduction Recently, there has been a surge of excitement in developing chatbots for various purposes in research and enterprise. Data-driven approaches offered by common bot building platforms (e.g. Google Dialogflow, Amazon Alexa Skil...	AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average
954c4756e293fd5c26dc50dc74f505cc94b3f8cc	954c4756e293fd5c26dc50dc74f505cc94b3f8cc_0	Q: What are dilated convolutions? Text: Introduction Keyword spotting (KWS) aims at detecting a pre-defined keyword or set of keywords in a continuous stream of audio. In particular, wake-word detection is an increasingly important application of KWS, used to initiate an interaction with a voice interface. In practice,...	Similar to standard convolutional networks but instead they skip some input values effectively operating on a broader scale.
ee279ace5bc69d15e640da967bd4214fe264aa1a	ee279ace5bc69d15e640da967bd4214fe264aa1a_0	Q: what was the evaluation metrics studied in this work? Text: Introduction Knowledge graphs are a vital source for disambiguation and discovery in various tasks such as question answering BIBREF0 , information extraction BIBREF1 and search BIBREF2 . They are, however, known to suffer from data quality issues BIBREF3 ....	mean rank (MR), mean reciprocal rank (MRR), as well as Hits@1, Hits@3, and Hits@10
dac2591f19f5bbac3d4a7fa038ff7aa09f6f0d96	dac2591f19f5bbac3d4a7fa038ff7aa09f6f0d96_0	Q: what are the three methods presented in the paper? Text: Introduction The Explanation Regeneration shared task asked participants to develop methods to reconstruct gold explanations for elementary science questions BIBREF1, using a new corpus of gold explanations BIBREF2 that provides supervision and instrumentation...	Optimized TF-IDF, iterated TF-IDF, BERT re-ranking.