id stringlengths 10 10 | title stringlengths 12 156 | abstract stringlengths 279 2.02k | full_text dict | qas dict | figures_and_tables dict |
|---|---|---|---|---|---|
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... | {
"section_name": [
"Introduction",
"Related Work",
"Proposed Method",
"Proposed Method ::: Polarity Function",
"Proposed Method ::: Discourse Relation-Based Event Pairs",
"Proposed Method ::: Discourse Relation-Based Event Pairs ::: AL (Automatically Labeled Pairs)",
"Proposed Method ::: ... | {
"question": [
"What is the seed lexicon?",
"What are the results?",
"How are relations used to propagate polarity?",
"How big is the Japanese data?",
"What are labels available in dataset for supervision?",
"How big are improvements of supervszed learning results trained on smalled labeled d... | {
"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... |
2003.07723 | PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry | Most approaches to emotion analysis regarding social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions that have been shown to also include ... | {
"section_name": [
"",
" ::: ",
" ::: ::: ",
"Introduction",
"Related Work ::: Poetry in Natural Language Processing",
"Related Work ::: Emotion Annotation",
"Related Work ::: Emotion Classification",
"Data Collection",
"Data Collection ::: German",
"Data Collection ::: Engli... | {
"question": [
"Does the paper report macro F1?",
"How is the annotation experiment evaluated?",
"What are the aesthetic emotions formalized?"
],
"question_id": [
"3a9d391d25cde8af3334ac62d478b36b30079d74",
"8d8300d88283c73424c8f301ad9fdd733845eb47",
"48b12eb53e2d507343f19b8a667696a39b719... | {
"caption": [
"Figure 1: Temporal distribution of poetry corpora (Kernel Density Plots with bandwidth = 0.2).",
"Table 1: Statistics on our poetry corpora PO-EMO.",
"Table 2: Aesthetic Emotion Factors (Schindler et al., 2017).",
"Table 3: Cohen’s kappa agreement levels and normalized line-level emoti... |
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... | {
"section_name": [
"Introduction",
"A typology of community identity",
"Overview and intuition",
"Language-based formalization",
"Community-level measures",
"Applying the typology to Reddit",
"Community identity and user retention",
"Community-type and monthly retention",
"Communi... | {
"question": [
"Do they report results only on English data?",
"How do the various social phenomena examined manifest in different types of communities?",
"What patterns do they observe about how user engagement varies with the characteristics of a community?",
"How did the select the 300 Reddit comm... | {
"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... |
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 met(...TRUNCATED) | {"section_name":["Introduction","Related Work ::: Clinical Text Structuring","Related Work ::: Pre-t(...TRUNCATED) | {"question":["What data is the language model pretrained on?","What baselines is the proposed model (...TRUNCATED) | {"caption":["Fig. 1. An illustrative example of QA-CTS task.","TABLE I AN ILLUSTRATIVE EXAMPLE OF NA(...TRUNCATED) |
1811.00942 | Progress and Tradeoffs in Neural Language Models | "In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks fo(...TRUNCATED) | {"section_name":["Introduction","Background and Related Work","Experimental Setup","Hyperparameters (...TRUNCATED) | {"question":["What aspects have been compared between various language models?","what classic langua(...TRUNCATED) | {"caption":["Table 1: Comparison of neural language models on Penn Treebank and WikiText-103.","Figu(...TRUNCATED) |
1805.02400 | Stay On-Topic: Generating Context-specific Fake Restaurant Reviews | "Automatically generated fake restaurant reviews are a threat to online review systems. Recent resea(...TRUNCATED) | {"section_name":["Introduction","Background","System Model","Attack Model","Generative Model"],"para(...TRUNCATED) | {"question":["Which dataset do they use a starting point in generating fake reviews?","Do they use a(...TRUNCATED) | {"caption":["Fig. 1: Näıve text generation with NMT vs. generation using our NTM model. Repetitiv(...TRUNCATED) |
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) | {"section_name":["Introduction","The Task and the Model","Dataset and Training Task","The Model","Ob(...TRUNCATED) | {"question":["Which baselines did they compare?","How many attention layers are there in their model(...TRUNCATED) | {"caption":["Figure 2: Representation of the propagation of the relevance from the output to the inp(...TRUNCATED) |
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) | {"section_name":["Introduction","Background ::: Geometric Bias Mitigation","Background ::: Geometric(...TRUNCATED) | {"question":["How is embedding quality assessed?","What are the three measures of bias which are red(...TRUNCATED) | {"caption":["Figure 1: Word embedding semantic quality benchmarks for each bias mitigation method (h(...TRUNCATED) |
1912.02481 | Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor\`ub\'a and Twi | "The success of several architectures to learn semantic representations from unannotated text and th(...TRUNCATED) | {"section_name":["Introduction","Related Work","Languages under Study ::: Yorùbá","Languages under(...TRUNCATED) | {"question":["What turn out to be more important high volume or high quality data?","How much is mod(...TRUNCATED) | {"caption":["Table 1: Summary of the corpora used in the analysis. The last 3 columns indicate in wh(...TRUNCATED) |
1810.04528 | Is there Gender bias and stereotype in Portuguese Word Embeddings? | "In this work, we propose an analysis of the presence of gender bias associated with professions in (...TRUNCATED) | {"section_name":["Introduction","Related Work","Portuguese Embedding","Proposed Approach","Experimen(...TRUNCATED) | {"question":["Does this paper target European or Brazilian Portuguese?","What were the word embeddin(...TRUNCATED) | {"caption":["Fig. 1. Proposal","Fig. 2. Extreme Analogies"],"file":["3-Figure1-1.png","5-Figure2-1.p(...TRUNCATED) |
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