id stringlengths 10 10 | title stringlengths 19 145 | abstract stringlengths 273 1.91k | full_text dict | qas dict | figures_and_tables dict | question sequence | retrieval_gt sequence | answer_gt sequence | __index_level_0__ int64 0 887 |
|---|---|---|---|---|---|---|---|---|---|
1909.00694 | Minimally Supervised Learning of Affective Events Using Discourse Relations | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | {
"paragraphs": [
[
"Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to vario... | {
"answers": [
{
"annotation_id": [
"31e85022a847f37c15fd0415f3c450c74c8e4755",
"95da0a6e1b08db74a405c6a71067c9b272a50ff5"
],
"answer": [
{
"evidence": [
"The seed lexicon consists of positive and negative predicates. If the predicate of an extracted... | {
"caption": [
"Figure 1: An overview of our method. We focus on pairs of events, the former events and the latter events, which are connected with a discourse relation, CAUSE or CONCESSION. Dropped pronouns are indicated by brackets in English translations. We divide the event pairs into three types: AL, CA, and... | [
"What is the seed lexicon?",
"What are the results?",
"How are relations used to propagate polarity?",
"How big is the Japanese data?",
"How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach?",
"How does their mode... | [
[
"1909.00694-Proposed Method ::: Discourse Relation-Based Event Pairs-1"
],
[
"1909.00694-Experiments ::: Model Configurations-2",
"1909.00694-5-Table4-1.png",
"1909.00694-Experiments ::: Model Configurations-0",
"1909.00694-5-Table3-1.png"
],
[
"1909.00694-Proposed Method ::: Disco... | [
"a vocabulary of positive and negative predicates that helps determine the polarity score of an event",
"Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.9... | 0 |
1705.09665 | Community Identity and User Engagement in a Multi-Community Landscape | A community's identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities... | {
"paragraphs": [
[
"“If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.”",
"",
"— Italo Calvino, Invisible Cities",
"A community's identity—defined through the... | {
"answers": [
{
"annotation_id": [
"04ae0cc420f69540ca11707ab8ecc07a89f803f7",
"31d8f8ed7ba40b27c480f7caf7cfb48fba47bb07"
],
"answer": [
{
"evidence": [
"Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for w... | {
"caption": [
"Figure 1: A: Within a community certain words are more community-specific and temporally volatile than others. For instance, words like onesies are highly specific to the BabyBumps community (top left corner), while words like easter are temporally ephemeral. B: Extending these word-level measures... | [
"How do the various social phenomena examined manifest in different types of communities?",
"How did the select the 300 Reddit communities for comparison?"
] | [
[
"1705.09665-Community-type and user tenure-0",
"1705.09665-Community-type and monthly retention-0"
],
[
"1705.09665-Applying the typology to Reddit-3"
]
] | [
"Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average num... | 2 |
1908.06606 | Question Answering based Clinical Text Structuring Using Pre-trained Language Model | Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QA-CTS) task t... | {
"paragraphs": [
[
"Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at du... | {
"answers": [
{
"annotation_id": [
"0ab604dbe114dba174da645cc06a713e12a1fd9d",
"1f1495d06d0abe86ee52124ec9f2f0b25a536147"
],
"answer": [
{
"evidence": [
"To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow ... | {
"caption": [
"Fig. 1. An illustrative example of QA-CTS task.",
"TABLE I AN ILLUSTRATIVE EXAMPLE OF NAMED ENTITY FEATURE TAGS",
"Fig. 2. The architecture of our proposed model for QA-CTS task",
"TABLE II STATISTICS OF DIFFERENT TYPES OF QUESTION ANSWER INSTANCES",
"TABLE V COMPARATIVE RESULTS FO... | [
"How is the clinical text structuring task defined?",
"Is all text in this dataset a question, or are there unrelated sentences in between questions?",
"How many questions are in the dataset?"
] | [
[
"1908.06606-Introduction-0",
"1908.06606-1-Figure1-1.png",
"1908.06606-Introduction-1",
"1908.06606-Introduction-3"
],
[
"1908.06606-Experimental Studies ::: Dataset and Evaluation Metrics-0"
],
[
"1908.06606-Experimental Studies ::: Dataset and Evaluation Metrics-0"
]
] | [
"CTS is extracting structural data from medical research data (unstructured). Authors define QA-CTS task that aims to discover most related text from original text.",
"the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include ... | 3 |
1811.00942 | Progress and Tradeoffs in Neural Language Models | In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cost in performance, in terms of inference latency and ene... | {
"paragraphs": [
[
"Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances... | {
"answers": [
{
"annotation_id": [
"c17796e0bd3bfcc64d5a8e844d23d8d39274af6b"
],
"answer": [
{
"evidence": [
"For each model, we examined word-level perplexity, R@3 in next-word prediction, latency (ms/q), and energy usage (mJ/q). To explore the perplexity–... | {
"caption": [
"Table 1: Comparison of neural language models on Penn Treebank and WikiText-103.",
"Figure 1: Log perplexity–recall error with KN-5.",
"Figure 2: Log perplexity–recall error with QRNN.",
"Table 2: Language modeling results on performance and model quality."
],
"file": [
"3-Tabl... | [
"What aspects have been compared between various language models?"
] | [
[
"1811.00942-Experimental Setup-2"
]
] | [
"Quality measures using perplexity and recall, and performance measured using latency and energy usage. "
] | 4 |
1907.05664 | Saliency Maps Generation for Automatic Text Summarization | "Saliency map generation techniques are at the forefront of explainable AI literature for a broad ra(...TRUNCATED) | {"paragraphs":[["Ever since the LIME algorithm BIBREF0 , \"explanation\" techniques focusing on find(...TRUNCATED) | {"answers":[{"annotation_id":["0850b7c0555801d057062480de6bb88adb81cae3","93216bca45711b73083372495d(...TRUNCATED) | {"caption":["Figure 2: Representation of the propagation of the relevance from the output to the inp(...TRUNCATED) | [
"How many attention layers are there in their model?"
] | [
[
"1907.05664-The Model-0"
]
] | [
"one"
] | 6 |
1910.14497 | Probabilistic Bias Mitigation in Word Embeddings | "It has been shown that word embeddings derived from large corpora tend to incorporate biases presen(...TRUNCATED) | {"paragraphs":[["Word embeddings, or vector representations of words, are an important component of (...TRUNCATED) | {"answers":[{"annotation_id":["50e0354ccb4d7d6fda33c34e69133daaa8978a2f","eb66f1f7e89eca5dcf2ae6ef45(...TRUNCATED) | {"caption":["Figure 1: Word embedding semantic quality benchmarks for each bias mitigation method (h(...TRUNCATED) | [
"What are the three measures of bias which are reduced in experiments?"
] | [["1910.14497-Background ::: Geometric Bias Mitigation ::: RIPA-0","1910.14497-4-Table1-1.png","1910(...TRUNCATED) | [
"RIPA, Neighborhood Metric, WEAT"
] | 7 |
2002.02224 | Citation Data of Czech Apex Courts | "In this paper, we introduce the citation data of the Czech apex courts (Supreme Court, Supreme Admi(...TRUNCATED) | {"paragraphs":[["Analysis of the way court decisions refer to each other provides us with important (...TRUNCATED) | {"answers":[{"annotation_id":["3bf5c275ced328b66fd9a07b30a4155fa476d779","ae80f5c5b782ad02d1dde21b73(...TRUNCATED) | {"caption":["Figure 1: NLP pipeline including the text segmentation, reference recognition and parsi(...TRUNCATED) | [
"How big is the dataset?"
] | [
[
"2002.02224-Results-0"
]
] | [
"903019 references"
] | 10 |
2003.07433 | "LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessm(...TRUNCATED) | "Veteran mental health is a significant national problem as large number of veterans are returning f(...TRUNCATED) | {"paragraphs":[["Combat veterans diagnosed with PTSD are substantially more likely to engage in a nu(...TRUNCATED) | {"answers":[{"annotation_id":["4e3a79dc56c6f39d1bec7bac257c57f279431967"],"answer":[{"evidence":[],"(...TRUNCATED) | {"caption":["Fig. 1. Overview of our framework","Fig. 2. WordStat dictionary sample","TABLE I DRYHOO(...TRUNCATED) | [
"How is the intensity of the PTSD established?"
] | [["2003.07433-Demographics of Clinically Validated PTSD Assessment Tools-4","2003.07433-Demographics(...TRUNCATED) | [
"defined into four categories from high risk, moderate risk, to low risk"
] | 11 |
1904.09678 | UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages | "In this paper, we introduce UniSent a universal sentiment lexica for 1000 languages created using a(...TRUNCATED) | {"paragraphs":[["Sentiment classification is an important task which requires either word level or d(...TRUNCATED) | {"answers":[{"annotation_id":["97009bed24107de806232d7cf069f51053d7ba5e","e38ed05ec140abd97006a8fa7a(...TRUNCATED) | {"caption":["Figure 1: Neighbors of word ’sensual’ in Spanish, in bible embedding graph (a) and (...TRUNCATED) | [
"how is quality measured?"
] | [
[
"1904.09678-4-Table1-1.png"
]
] | ["Accuracy and the macro-F1 (averaged F1 over positive and negative classes) are used as a measure o(...TRUNCATED) | 13 |
1910.04269 | Spoken Language Identification using ConvNets | "Language Identification (LI) is an important first step in several speech processing systems. With (...TRUNCATED) | {"paragraphs":[["Language Identification (LI) is a problem which involves classifying the language b(...TRUNCATED) | {"answers":[{"annotation_id":["32dee5de8cb44c67deef309c16e14e0634a7a95e"],"answer":[{"evidence":["FL(...TRUNCATED) | {"caption":["Table 2: Architecture of the 1D-ConvNet model","Fig. 1: Effect of hyperparameter variat(...TRUNCATED) | [
"What is the accuracy reported by state-of-the-art methods?"
] | [
[
"1910.04269-Related Work-6"
]
] | ["Answer with content missing: (Table 1)\nPrevious state-of-the art on same dataset: ResNet50 89% (6(...TRUNCATED) | 15 |
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