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kothasuhas
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qasper-eval

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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...
Introduction 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 various natural language ...
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...
1.1em ::: 1.1.1em ::: ::: 1.1.1.1em Thomas Haider$^{1,3}$, Steffen Eger$^2$, Evgeny Kim$^3$, Roman Klinger$^3$, Winfried Menninghaus$^1$ $^{1}$Department of Language and Literature, Max Planck Institute for Empirical Aesthetics $^{2}$NLLG, Department of Computer Science, Technische Universitat Darmstadt $^{...
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...
Introduction “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 common interests and shared experiences of ...
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...
{ "section_name": [ "Introduction", "Related Work ::: Clinical Text Structuring", "Related Work ::: Pre-trained Language Model", "Question Answering based Clinical Text Structuring", "The Proposed Model for QA-CTS Task", "The Proposed Model for QA-CTS Task ::: Contextualized Representation of ...
{ "question": [ "What data is the language model pretrained on?", "What baselines is the proposed model compared against?", "How is the clinical text structuring task defined?", "What are the specific tasks being unified?", "Is all text in this dataset a question, or are there unrelated sentences ...
{ "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...
Introduction 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 during the surgery, or...
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...
{ "section_name": [ "Introduction", "Background and Related Work", "Experimental Setup", "Hyperparameters and Training", "Infrastructure", "Results and Discussion", "Conclusion" ], "paragraphs": [ [ "Deep learning has unquestionably advanced the state of the art in many natur...
{ "question": [ "What aspects have been compared between various language models?", "what classic language models are mentioned in the paper?", "What is a commonly used evaluation metric for language models?" ], "question_id": [ "dd155f01f6f4a14f9d25afc97504aefdc6d29c13", "a9d530d68fb45b52d9ba...
{ "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...
Introduction 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 in neural language ...
1805.02400
Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
Automatically generated fake restaurant reviews are a threat to online review systems. Recent research has shown that users have difficulties in detecting machine-generated fake reviews hiding among real restaurant reviews. The method used in this work (char-LSTM ) has one drawback: it has difficulties staying in conte...
{ "section_name": [ "Introduction", "Background", "System Model", "Attack Model", "Generative Model" ], "paragraphs": [ [ "Automatically generated fake reviews have only recently become natural enough to fool human readers. Yao et al. BIBREF0 use a deep neural network (a so-called 2-...
{ "question": [ "Which dataset do they use a starting point in generating fake reviews?", "Do they use a pretrained NMT model to help generating reviews?", "How does using NMT ensure generated reviews stay on topic?", "What kind of model do they use for detection?", "Does their detection tool work...
{ "caption": [ "Fig. 1: Näıve text generation with NMT vs. generation using our NTM model. Repetitive patterns are underlined. Contextual words are italicized. Both examples here are generated based on the context given in Example 1.", "Table 1: Six different parametrizations of our NMT reviews and one examp...
Introduction Automatically generated fake reviews have only recently become natural enough to fool human readers. Yao et al. BIBREF0 use a deep neural network (a so-called 2-layer LSTM BIBREF1 ) to generate fake reviews, and concluded that these fake reviews look sufficiently genuine to fool native English speakers. Th...
1907.05664
Saliency Maps Generation for Automatic Text Summarization
Saliency map generation techniques are at the forefront of explainable AI literature for a broad range of machine learning applications. Our goal is to question the limits of these approaches on more complex tasks. In this paper we apply Layer-Wise Relevance Propagation (LRP) to a sequence-to-sequence attention model t...
{ "section_name": [ "Introduction", "The Task and the Model", "Dataset and Training Task", "The Model", "Obtained Summaries", "Layer-Wise Relevance Propagation", "Mathematical Description", "Generation of the Saliency Maps", "Experimental results", "First Observations", "Va...
{ "question": [ "Which baselines did they compare?", "How many attention layers are there in their model?", "Is the explanation from saliency map correct?" ], "question_id": [ "6e2ad9ad88cceabb6977222f5e090ece36aa84ea", "aacb0b97aed6fc6a8b471b8c2e5c4ddb60988bf5", "710c1f8d4c137c8dad9972f5c...
{ "caption": [ "Figure 2: Representation of the propagation of the relevance from the output to the input. It passes through the decoder and attention mechanism for each previous decoding time-step, then is passed onto the encoder which takes into account the relevance transiting in both direction due to the bidi...
Introduction Ever since the LIME algorithm BIBREF0 , "explanation" techniques focusing on finding the importance of input features in regard of a specific prediction have soared and we now have many ways of finding saliency maps (also called heat-maps because of the way we like to visualize them). We are interested in ...
1910.14497
Probabilistic Bias Mitigation in Word Embeddings
It has been shown that word embeddings derived from large corpora tend to incorporate biases present in their training data. Various methods for mitigating these biases have been proposed, but recent work has demonstrated that these methods hide but fail to truly remove the biases, which can still be observed in word n...
{ "section_name": [ "Introduction", "Background ::: Geometric Bias Mitigation", "Background ::: Geometric Bias Mitigation ::: WEAT", "Background ::: Geometric Bias Mitigation ::: RIPA", "Background ::: Geometric Bias Mitigation ::: Neighborhood Metric", "A Probabilistic Framework for Bias Miti...
{ "question": [ "How is embedding quality assessed?", "What are the three measures of bias which are reduced in experiments?", "What are the probabilistic observations which contribute to the more robust algorithm?" ], "question_id": [ "47726be8641e1b864f17f85db9644ce676861576", "8958465d1eaf8...
{ "caption": [ "Figure 1: Word embedding semantic quality benchmarks for each bias mitigation method (higher is better). See Jastrzkebski et al. [11] for details of each metric.", "Table 1: Remaining Bias (as measured by RIPA and Neighborhood metrics) in fastText embeddings for baseline (top two rows) and our...
Introduction Word embeddings, or vector representations of words, are an important component of Natural Language Processing (NLP) models and necessary for many downstream tasks. However, word embeddings, including embeddings commonly deployed for public use, have been shown to exhibit unwanted societal stereotypes and ...
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 the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually ...
{ "section_name": [ "Introduction", "Related Work", "Languages under Study ::: Yorùbá", "Languages under Study ::: Twi", "Data", "Data ::: Training Corpora", "Data ::: Evaluation Test Sets ::: Yorùbá.", "Data ::: Evaluation Test Sets ::: Twi", "Semantic Representations", "Seman...
{ "question": [ "What turn out to be more important high volume or high quality data?", "How much is model improved by massive data and how much by quality?", "What two architectures are used?" ], "question_id": [ "347e86893e8002024c2d10f618ca98e14689675f", "10091275f777e0c2890c3ac0fd0a7d8e266...
{ "caption": [ "Table 1: Summary of the corpora used in the analysis. The last 3 columns indicate in which dataset (C1, C2 or C3) are the different sources included (see text, Section 5.2.).", "Table 2: Number of tokens per named entity type in the Global Voices Yorùbá corpus.", "Table 3: FastText embed...
Introduction In recent years, word embeddings BIBREF0, BIBREF1, BIBREF2 have been proven to be very useful for training downstream natural language processing (NLP) tasks. Moreover, contextualized embeddings BIBREF3, BIBREF4 have been shown to further improve the performance of NLP tasks such as named entity recognitio...
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 Portuguese word embeddings. The objective of this work is to study gender implications related to stereotyped professions for women and men in the context of the Portuguese language.
{ "section_name": [ "Introduction", "Related Work", "Portuguese Embedding", "Proposed Approach", "Experiments", "Final Remarks" ], "paragraphs": [ [ "Recently, the transformative potential of machine learning (ML) has propelled ML into the forefront of mainstream media. In Brazil...
{ "question": [ "Does this paper target European or Brazilian Portuguese?", "What were the word embeddings trained on?", "Which word embeddings are analysed?" ], "question_id": [ "519db0922376ce1e87fcdedaa626d665d9f3e8ce", "99a10823623f78dbff9ccecb210f187105a196e9", "09f0dce416a1e40cc6a24a...
{ "caption": [ "Fig. 1. Proposal", "Fig. 2. Extreme Analogies" ], "file": [ "3-Figure1-1.png", "5-Figure2-1.png" ] }
Introduction Recently, the transformative potential of machine learning (ML) has propelled ML into the forefront of mainstream media. In Brazil, the use of such technique has been widely diffused gaining more space. Thus, it is used to search for patterns, regularities or even concepts expressed in data sets BIBREF0 , ...
2002.02224
Citation Data of Czech Apex Courts
In this paper, we introduce the citation data of the Czech apex courts (Supreme Court, Supreme Administrative Court and Constitutional Court). This dataset was automatically extracted from the corpus of texts of Czech court decisions - CzCDC 1.0. We obtained the citation data by building the natural language processing...
{ "section_name": [ "Introduction", "Related work ::: Legal Citation Analysis", "Related work ::: Reference Recognition", "Related work ::: Data Availability", "Related work ::: Document Segmentation", "Methodology", "Methodology ::: Dataset and models ::: CzCDC 1.0 dataset", "Methodol...
{ "question": [ "Did they experiment on this dataset?", "How is quality of the citation measured?", "How big is the dataset?" ], "question_id": [ "ac706631f2b3fa39bf173cd62480072601e44f66", "8b71ede8170162883f785040e8628a97fc6b5bcb", "fa2a384a23f5d0fe114ef6a39dced139bddac20e" ], "nlp_b...
{ "caption": [ "Figure 1: NLP pipeline including the text segmentation, reference recognition and parsing of references to the specific document", "Table 1: Model performance", "Table 2: References sorted by categories, unlinked", "Table 3: References linked with texts in CzCDC" ], "file": [ "...
Introduction Analysis of the way court decisions refer to each other provides us with important insights into the decision-making process at courts. This is true both for the common law courts and for their counterparts in the countries belonging to the continental legal system. Citation data can be used for both quali...
2003.07433
LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment
Veteran mental health is a significant national problem as large number of veterans are returning from the recent war in Iraq and continued military presence in Afghanistan. While significant existing works have investigated twitter posts-based Post Traumatic Stress Disorder (PTSD) assessment using blackbox machine lea...
{ "section_name": [ "Introduction", "Overview", "Related Works", "Demographics of Clinically Validated PTSD Assessment Tools", "Twitter-based PTSD Detection", "Twitter-based PTSD Detection ::: Data Collection", "Twitter-based PTSD Detection ::: Pre-processing", "Twitter-based PTSD Dete...
{ "question": [ "Do they evaluate only on English datasets?", "Do the authors mention any possible confounds in this study?", "How is the intensity of the PTSD established?", "How is LIWC incorporated into this system?", "How many twitter users are surveyed using the clinically validated survey?",...
{ "caption": [ "Fig. 1. Overview of our framework", "Fig. 2. WordStat dictionary sample", "TABLE I DRYHOOTCH CHOSEN PTSD ASSESSMENT SURVEYS (D: DOSPERT, B: BSSS AND V: VIAS) DEMOGRAPHICS", "TABLE II SAMPLE DRYHOOTCH CHOSEN QUESTIONS FROM DOSPERT", "Fig. 3. Each 210 users’ average tweets per month"...
Introduction Combat veterans diagnosed with PTSD are substantially more likely to engage in a number of high risk activities including engaging in interpersonal violence, attempting suicide, committing suicide, binge drinking, and drug abuse BIBREF0. Despite improved diagnostic screening, outpatient mental health and i...
2003.12218
Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
We created this CORD-19-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020- 03-13). This CORD-19-NER dataset covers 74 fine-grained named entity types. It is automatically generated by combining the annotation results from four sources: (...
{ "section_name": [ "Introduction", "CORD-19-NER Dataset ::: Corpus", "CORD-19-NER Dataset ::: NER Methods", "Results ::: NER Annotation Results", "Results ::: Top-Frequent Entity Summarization", "Conclusion", "Acknowledgment" ], "paragraphs": [ [ "Coronavirus disease 2019 (C...
{ "question": [ "Did they experiment with the dataset?", "What is the size of this dataset?", "Do they list all the named entity types present?" ], "question_id": [ "ce6201435cc1196ad72b742db92abd709e0f9e8d", "928828544e38fe26c53d81d1b9c70a9fb1cc3feb", "4f243056e63a74d1349488983dc1238228ca...
{ "caption": [ "Table 1: Performance comparison on three major biomedical entity types in COVID-19 corpus.", "Figure 1: Examples of the annotation results with CORD-NER system.", "Figure 2: Annotation result comparison with other NER methods.", "Table 2: Examples of the most frequent entities annotate...
Introduction Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in 2019 in Wuhan, Central China, and has since spread globally, resulting in the 2019–2020 coronavirus pandemic. On March 16th, 2020, researc...
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 an English sentiment lexicon and a massively parallel corpus in the Bible domain. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of number of covered languages, including many low ...
{ "section_name": [ "Introduction", "Method", "Experimental Setup", "Results", "Conclusion" ], "paragraphs": [ [ "Sentiment classification is an important task which requires either word level or document level sentiment annotations. Such resources are available for at most 136 langu...
{ "question": [ "how is quality measured?", "how many languages exactly is the sentiment lexica for?", "what sentiment sources do they compare with?" ], "question_id": [ "8f87215f4709ee1eb9ddcc7900c6c054c970160b", "b04098f7507efdffcbabd600391ef32318da28b3", "8fc14714eb83817341ada708b9a0b6b...
{ "caption": [ "Figure 1: Neighbors of word ’sensual’ in Spanish, in bible embedding graph (a) and twitter embedding graph (b). Our unsupervised drift weighting method found this word in Spanish to be the most changing word from bible context to the twitter context. Looking more closely at the neighbors, the word...
Introduction Sentiment classification is an important task which requires either word level or document level sentiment annotations. Such resources are available for at most 136 languages BIBREF0 , preventing accurate sentiment classification in a low resource setup. Recent research efforts on cross-lingual transfer le...
2003.06651
Word Sense Disambiguation for 158 Languages using Word Embeddings Only
Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality ...
{ "section_name": [ "", " ::: ", " ::: ::: ", "Introduction", "Related Work", "Algorithm for Word Sense Induction", "Algorithm for Word Sense Induction ::: SenseGram: A Baseline Graph-based Word Sense Induction Algorithm", "Algorithm for Word Sense Induction ::: egvi (Ego-Graph Vector...
{ "question": [ "Is the method described in this work a clustering-based method?", "How are the different senses annotated/labeled? ", "Was any extrinsic evaluation carried out?" ], "question_id": [ "d94ac550dfdb9e4bbe04392156065c072b9d75e1", "eeb6e0caa4cf5fdd887e1930e22c816b99306473", "3c...
{ "caption": [ "Table 1: Top nearest neighbours of the fastText vector of the word Ruby are clustered according to various senses of this word: programming language, gem, first name, color, but also its spelling variations (typeset in black color).", "Figure 1: The graph of nearest neighbours of the word Ruby...
1.1em ::: 1.1.1em ::: ::: 1.1.1.1em ru=russian $^1$Skolkovo Institute of Science and Technology, Moscow, Russia v.logacheva@skoltech.ru $^2$Ural Federal University, Yekaterinburg, Russia $^3$Universität Hamburg, Hamburg, Germany $^4$Universität Mannheim, Mannheim, Germany $^5$University of Oslo, Oslo, ...
1910.04269
Spoken Language Identification using ConvNets
Language Identification (LI) is an important first step in several speech processing systems. With a growing number of voice-based assistants, speech LI has emerged as a widely researched field. To approach the problem of identifying languages, we can either adopt an implicit approach where only the speech for a langua...
{ "section_name": [ "Introduction", "Related Work", "Proposed Method ::: Motivations", "Proposed Method ::: Description of Features", "Proposed Method ::: Model Description", "Proposed Method ::: Model Details: 1D ConvNet", "Proposed Method ::: Model Details: 1D ConvNet ::: Hyperparameter ...
{ "question": [ "Does the model use both spectrogram images and raw waveforms as features?", "Is the performance compared against a baseline model?", "What is the accuracy reported by state-of-the-art methods?" ], "question_id": [ "dc1fe3359faa2d7daa891c1df33df85558bc461b", "922f1b740f8b13fdc8...
{ "caption": [ "Table 2: Architecture of the 1D-ConvNet model", "Fig. 1: Effect of hyperparameter variation of the hyperparameter on the classification accuracy for the case of 1D-ConvNet. Orange colored violin plots show the most favored choice of the hyperparameter and blue shows otherwise. One dot represen...
Introduction Language Identification (LI) is a problem which involves classifying the language being spoken by a speaker. LI systems can be used in call centers to route international calls to an operator who is fluent in that identified language BIBREF0. In speech-based assistants, LI acts as the first step which choo...
1906.00378
Unsupervised Bilingual Lexicon Induction from Mono-lingual Multimodal Data
Bilingual lexicon induction, translating words from the source language to the target language, is a long-standing natural language processing task. Recent endeavors prove that it is promising to employ images as pivot to learn the lexicon induction without reliance on parallel corpora. However, these vision-based appr...
{ "section_name": [ "Introduction", "Related Work", "Unsupervised Bilingual Lexicon Induction", "Multi-lingual Image Caption Model", "Visual-guided Word Representation", "Word Translation Prediction", "Datasets", "Experimental Setup", "Evaluation of Multi-lingual Image Caption", ...
{ "question": [ "Which vision-based approaches does this approach outperform?", "What baseline is used for the experimental setup?", "Which languages are used in the multi-lingual caption model?" ], "question_id": [ "591231d75ff492160958f8aa1e6bfcbbcd85a776", "9e805020132d950b54531b1a2620f6155...
{ "caption": [ "Figure 1: Comparison of previous vision-based approaches and our proposed approach for bilingual lexicon induction. Best viewed in color.", "Figure 2: Multi-lingual image caption model. The source and target language caption models share the same image encoder and language decoder, which enfor...
Introduction The bilingual lexicon induction task aims to automatically build word translation dictionaries across different languages, which is beneficial for various natural language processing tasks such as cross-lingual information retrieval BIBREF0 , multi-lingual sentiment analysis BIBREF1 , machine translation B...
1912.13072
AraNet: A Deep Learning Toolkit for Arabic Social Media
We describe AraNet, a collection of deep learning Arabic social media processing tools. Namely, we exploit an extensive host of publicly available and novel social media datasets to train bidirectional encoders from transformer models (BERT) to predict age, dialect, gender, emotion, irony, and sentiment. AraNet deliver...
{ "section_name": [ "Introduction", "Introduction ::: ", "Methods", "Data and Models ::: Age and Gender", "Data and Models ::: Age and Gender ::: ", "Data and Models ::: Dialect", "Data and Models ::: Emotion", "Data and Models ::: Irony", "Data and Models ::: Sentiment", "AraN...
{ "question": [ "Did they experiment on all the tasks?", "What models did they compare to?", "What datasets are used in training?" ], "question_id": [ "2419b38624201d678c530eba877c0c016cccd49f", "b99d100d17e2a121c3c8ff789971ce66d1d40a4d", "578d0b23cb983b445b1a256a34f969b34d332075" ], "...
{ "caption": [ "Figure 1: A map of Arab countries. Our different datasets cover varying regions of the Arab world as we describe in each section.", "Table 1: Distribution of age and gender classes in our Arab-Tweet data splits", "Table 2: Model performance in accuracy of Arab-Tweet age and gender classifi...
Introduction The proliferation of social media has made it possible to study large online communities at scale, thus making important discoveries that can facilitate decision making, guide policies, improve health and well-being, aid disaster response, etc. The wide host of languages, languages varieties, and dialects ...
1712.09127
Generative Adversarial Nets for Multiple Text Corpora
Generative adversarial nets (GANs) have been successfully applied to the artificial generation of image data. In terms of text data, much has been done on the artificial generation of natural language from a single corpus. We consider multiple text corpora as the input data, for which there can be two applications of G...
{ "section_name": [ "Introduction", "Literature Review", "Models and Algorithms", "weGAN: Training cross-corpus word embeddings", "deGAN: Generating document embeddings for multi-corpus text data", "Experiments", "The CNN data set", "The TIME data set", "The 20 Newsgroups data set"...
{ "question": [ "Which GAN do they use?", "Do they evaluate grammaticality of generated text?", "Which corpora do they use?" ], "question_id": [ "6548db45fc28e8a8b51f114635bad14a13eaec5b", "4c4f76837d1329835df88b0921f4fe8bda26606f", "819d2e97f54afcc7cdb3d894a072bcadfba9b747" ], "nlp_ba...
{ "caption": [ "Figure 1: Model structure of weGAN.", "Figure 2: Model structure of deGAN.", "Table 1: A comparison between word2vec and weGAN in terms of the Rand index and the classification accuracy for the CNN data set.", "Table 3: A comparison between word2vec and deGAN in terms of the accuracy f...
Introduction Generative adversarial nets (GAN) (Goodfellow et al., 2014) belong to a class of generative models which are trainable and can generate artificial data examples similar to the existing ones. In a GAN model, there are two sub-models simultaneously trained: a generative model INLINEFORM0 from which artificia...
2001.00137
Stacked DeBERT: All Attention in Incomplete Data for Text Classification
In this paper, we propose Stacked DeBERT, short for Stacked Denoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, by designing a novel encoding scheme in BERT, a powerful language representation model solely based o...
{ "section_name": [ "Introduction", "Proposed model", "Dataset ::: Twitter Sentiment Classification", "Dataset ::: Intent Classification from Text with STT Error", "Experiments ::: Baseline models", "Experiments ::: Baseline models ::: NLU service platforms", "Experiments ::: Baseline mode...
{ "question": [ "Do they report results only on English datasets?", "How do the authors define or exemplify 'incorrect words'?", "How many vanilla transformers do they use after applying an embedding layer?", "Do they test their approach on a dataset without incomplete data?", "Should their approa...
{ "caption": [ "Figure 1: The proposed model Stacked DeBERT is organized in three layers: embedding, conventional bidirectional transformers and denoising bidirectional transformer.", "Table 1: Types of mistakes on the Twitter dataset.", "Table 2: Examples of original tweets and their corrected version.",...
Introduction Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. Th...
1910.03042
Gunrock: A Social Bot for Complex and Engaging Long Conversations
Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazon-selected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open domains. In this paper, we introduce some innovative system designs and...
{ "section_name": [ "Introduction", "System Architecture", "System Architecture ::: Automatic Speech Recognition", "System Architecture ::: Natural Language Understanding", "System Architecture ::: Dialog Manager", "System Architecture ::: Knowledge Databases", "System Architecture ::: Nat...
{ "question": [ "What is the sample size of people used to measure user satisfaction?", "What are all the metrics to measure user engagement?", "What the system designs introduced?", "Do they specify the model they use for Gunrock?", "Do they gather explicit user satisfaction data on Gunrock?", ...
{ "caption": [ "Figure 1: Gunrock system architecture", "Figure 2: Mean user rating by mean number of words. Error bars show standard error.", "Figure 3: Mean user rating based on number of queries to Gunrock’s backstory. Error bars show standard error.", "Figure 4: Mean user rating based ’Has Pet’. E...
Introduction Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of prior chatbots BIBREF2, BIBREF3, BIBREF4 including inconsistency and difficulty...
2002.06644
Towards Detection of Subjective Bias using Contextualized Word Embeddings
Subjective bias detection is critical for applications like propaganda detection, content recommendation, sentiment analysis, and bias neutralization. This bias is introduced in natural language via inflammatory words and phrases, casting doubt over facts, and presupposing the truth. In this work, we perform comprehens...
{ "section_name": [ "Introduction", "Baselines and Approach", "Baselines and Approach ::: Baselines", "Baselines and Approach ::: Proposed Approaches", "Experiments ::: Dataset and Experimental Settings", "Experiments ::: Experimental Results", "Conclusion" ], "paragraphs": [ [ ...
{ "question": [ "Do the authors report only on English?", "What is the baseline for the experiments?", "Which experiments are perfomed?" ], "question_id": [ "830de0bd007c4135302138ffa8f4843e4915e440", "680dc3e56d1dc4af46512284b9996a1056f89ded", "bd5379047c2cf090bea838c67b6ed44773bcd56f" ...
{ "caption": [ "Table 1: Experimental Results for the Subjectivity Detection Task" ], "file": [ "2-Table1-1.png" ] }
Introduction In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. Writers and editors of texts like news and textbooks try to avoid the use of biased language, yet subjective bia...
1809.08731
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric for referenceless fluency evaluation of natural language generation output at the sentence level. We further introduce WPSLOR, a novel WordPie...
{ "section_name": [ "Introduction", "On Acceptability", "Method", "SLOR", "WordPieces", "WPSLOR", "Experiment", "Dataset", "LM Hyperparameters and Training", "Baseline Metrics", "Correlation and Evaluation Scores", "Results and Discussion", "Analysis I: Fluency Eval...
{ "question": [ "Is ROUGE their only baseline?", "what language models do they use?", "what questions do they ask human judges?" ], "question_id": [ "7aa8375cdf4690fc3b9b1799b0f5a9ec1c1736ed", "3ac30bd7476d759ea5d9a5abf696d4dfc480175b", "0e57a0983b4731eba9470ba964d131045c8c7ea7" ], "nl...
{ "caption": [ "Table 1: Example compressions from our dataset with their fluency scores; scores in [1, 3], higher is better.", "Table 2: Average fluency ratings for each compression system in the dataset by Toutanova et al. (2016).", "Table 3: Pearson correlation (higher is better) and MSE (lower is bett...
Introduction Producing sentences which are perceived as natural by a human addressee—a property which we will denote as fluency throughout this paper —is a crucial goal of all natural language generation (NLG) systems: it makes interactions more natural, avoids misunderstandings and, overall, leads to higher user satis...
1707.00995
An empirical study on the effectiveness of images in Multimodal Neural Machine Translation
In state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word. Recently, the effect...
{ "section_name": [ "Introduction", "Neural Machine Translation", "Text-based NMT", "Conditional GRU", "Multimodal NMT", "Attention-based Models", "Soft attention", "Hard Stochastic attention", "Local Attention", "Image attention optimization", "Experiments", "Training ...
{ "question": [ "What misbehavior is identified?", "What is the baseline used?", "Which attention mechanisms do they compare?" ], "question_id": [ "f0317e48dafe117829e88e54ed2edab24b86edb1", "ec91b87c3f45df050e4e16018d2bf5b62e4ca298", "f129c97a81d81d32633c94111018880a7ffe16d1" ], "nlp_...
{ "caption": [ "Figure 1: Die beiden Kinder spielen auf dem Spielplatz .", "Figure 2: Ein Junge sitzt auf und blickt aus einem Mikroskop .", "Figure 3: Ein Mann sitzt neben einem Computerbildschirm .", "Figure 4: Ein Mann in einem orangefarbenen Hemd und mit Helm .", "Figure 5: Ein Mädchen mit ei...
Introduction In machine translation, neural networks have attracted a lot of research attention. Recently, the attention-based encoder-decoder framework BIBREF0 , BIBREF1 has been largely adopted. In this approach, Recurrent Neural Networks (RNNs) map source sequences of words to target sequences. The attention mechani...
1809.04960
Unsupervised Machine Commenting with Neural Variational Topic Model
Article comments can provide supplementary opinions and facts for readers, thereby increase the attraction and engagement of articles. Therefore, automatically commenting is helpful in improving the activeness of the community, such as online forums and news websites. Previous work shows that training an automatic comm...
{ "section_name": [ "Introduction", "Machine Commenting", "Challenges", "Solutions", "Proposed Approach", "Retrieval-based Commenting", "Neural Variational Topic Model", "Training", "Datasets", "Implementation Details", "Baselines", "Retrieval Evaluation", "Generati...
{ "question": [ "Which paired corpora did they use in the other experiment?", "By how much does their system outperform the lexicon-based models?", "Which lexicon-based models did they compare with?", "How many comments were used?", "How many articles did they have?", "What news comment datase...
{ "caption": [ "Table 2: The performance of the unsupervised models and supervised models under the retrieval evaluation settings. (Recall@k, MRR: higher is better; MR: lower is better.)", "Table 3: The performance of the unsupervised models and supervised models under the generative evaluation settings. (MET...
Introduction Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining the opinions, and generating the natural lan...
1909.08402
Enriching BERT with Knowledge Graph Embeddings for Document Classification
In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the...
{ "section_name": [ "Introduction", "Related Work", "Dataset and Task", "Experiments", "Experiments ::: Metadata Features", "Experiments ::: Author Embeddings", "Experiments ::: Pre-trained German Language Model", "Experiments ::: Model Architecture", "Experiments ::: Implementatio...
{ "question": [ "By how much do they outperform standard BERT?", "What dataset do they use?", "How do they combine text representations with the knowledge graph embeddings?" ], "question_id": [ "f5cf8738e8d211095bb89350ed05ee7f9997eb19", "bed527bcb0dd5424e69563fba4ae7e6ea1fca26a", "aeab579...
{ "caption": [ "Table 1: Availability of additional data with respect to the dataset (relative numbers in parenthesis).", "Figure 1: Visualization of Wikidata embeddings for Franz Kafka (3D-projection with PCA)5. Nearest neighbours in original 200D space: Arthur Schnitzler, E.T.A Hoffmann and Hans Christian A...
Introduction With ever-increasing amounts of data available, there is an increase in the need to offer tooling to speed up processing, and eventually making sense of this data. Because fully-automated tools to extract meaning from any given input to any desired level of detail have yet to be developed, this task is sti...
1909.11189
Diachronic Topics in New High German Poetry
Statistical topic models are increasingly and popularly used by Digital Humanities scholars to perform distant reading tasks on literary data. It allows us to estimate what people talk about. Especially Latent Dirichlet Allocation (LDA) has shown its usefulness, as it is unsupervised, robust, easy to use, scalable, and...
{ "section_name": [ "Corpus", "Experiments", "Experiments ::: Topic Trends", "Experiments ::: Classification of Time Periods and Authorship", "Experiments ::: Conclusion & Future Work" ], "paragraphs": [ [ "The Digital Library in the TextGrid Repository represents an extensive collec...
{ "question": [ "What is the algorithm used for the classification tasks?", "Is the outcome of the LDA analysis evaluated in any way?", "What is the corpus used in the study?" ], "question_id": [ "bfa3776c30cb30e0088e185a5908e5172df79236", "a2a66726a5dca53af58aafd8494c4de833a06f14", "ee876...
{ "caption": [ "Fig. 1: 25 year Time Slices of Textgrid Poetry (1575–1925)", "Fig. 2: left: Topic 27 ’Virtue, Arts’ (Period: Enlightenment), right: Topic 55 ’Flowers, Spring, Garden’ (Period: Early Romanticism)", "Fig. 3: left: Topic 63 ’Song’ (Period: Romanticism), right: Topic 33 ’German Nation’ (Period...
Corpus The Digital Library in the TextGrid Repository represents an extensive collection of German texts in digital form BIBREF3. It was mined from http://zeno.org and covers a time period from the mid 16th century up to the first decades of the 20th century. It contains many important texts that can be considered as p...
1810.05320
Important Attribute Identification in Knowledge Graph
The knowledge graph(KG) composed of entities with their descriptions and attributes, and relationship between entities, is finding more and more application scenarios in various natural language processing tasks. In a typical knowledge graph like Wikidata, entities usually have a large number of attributes, but it is d...
{ "section_name": [ "The problem we solve in this paper", "Related Research", "What we propose and what we have done", "Our proposed Method", "Application Scenario", "FastText Introduction", "Matching", "Data introduction", "Data preprocessing", "Proposed method vs previous met...
{ "question": [ "What are the traditional methods to identifying important attributes?", "What do you use to calculate word/sub-word embeddings", "What user generated text data do you use?" ], "question_id": [ "cda4612b4bda3538d19f4b43dde7bc30c1eda4e5", "e12674f0466f8c0da109b6076d9939b30952c7d...
{ "caption": [ "Fig. 1. A typical product enquiry example on Alibaba.com", "Fig. 2. Each sentence obtained from the enquiry is scored against possible attributes under that category.", "Table 1. Proposed method vs other methods metrics: precision, recall and F1-score." ], "file": [ "5-Figure1-1.pn...
The problem we solve in this paper Knowledge graph(KG) has been proposed for several years and its most prominent application is in web search, for example, Google search triggers a certain entity card when a user's query matches or mentions an entity based on some statistical model. The core potential of a knowledge g...
2003.08529
Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections
Summarizing data samples by quantitative measures has a long history, with descriptive statistics being a case in point. However, as natural language processing methods flourish, there are still insufficient characteristic metrics to describe a collection of texts in terms of the words, sentences, or paragraphs they co...
{ "section_name": [ "Introduction", "Related Work", "Proposed Characteristic Metrics", "Proposed Characteristic Metrics ::: Diversity", "Proposed Characteristic Metrics ::: Density", "Proposed Characteristic Metrics ::: Homogeneity", "Simulations", "Simulations ::: Simulation Setup", ...
{ "question": [ "Did they propose other metrics?", "Which real-world datasets did they use?", "How did they obtain human intuitions?" ], "question_id": [ "b5c3787ab3784214fc35f230ac4926fe184d86ba", "9174aded45bc36915f2e2adb6f352f3c7d9ada8b", "a8f1029f6766bffee38a627477f61457b2d6ed5c" ], ...
{ "caption": [ "Figure 1: Visualization of the simulations including base setting, down-sampling, varying spreads, adding outliers, and multiple sub-clusters in 2-dimensional and 768-dimensional spaces.", "Figure 2: Diversity, density, and homogeneity metric values in each simulation scenario.", "Table 1:...
Introduction Characteristic metrics are a set of unsupervised measures that quantitatively describe or summarize the properties of a data collection. These metrics generally do not use ground-truth labels and only measure the intrinsic characteristics of data. The most prominent example is descriptive statistics that s...
1708.05873
What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016
There is surprisingly little known about agenda setting for international development in the United Nations (UN) despite it having a significant influence on the process and outcomes of development efforts. This paper addresses this shortcoming using a novel approach that applies natural language processing techniques ...
{ "section_name": [ "Introduction", "The UN General Debate and international development", "Estimation of topic models", "Topics in the UN General Debate", "Explaining the rhetoric", "Conclusion" ], "paragraphs": [ [ "Decisions made in international organisations are fundamental ...
{ "question": [ "What are the country-specific drivers of international development rhetoric?", "Is the dataset multilingual?", "How are the main international development topics that states raise identified?" ], "question_id": [ "a2103e7fe613549a9db5e65008f33cf2ee0403bd", "13b36644357870008d7...
{ "caption": [ "Fig. 1. Optimal model search. Semantic coherence and exclusivity results for a model search from 3 to 50 topics. Models above the regression line provide a better trade off. Largest positive residual is a 16-topic model.", "Fig. 2. Topic quality. 20 highest probability words for the 16-topic m...
Introduction Decisions made in international organisations are fundamental to international development efforts and initiatives. It is in these global governance arenas that the rules of the global economic system, which have a huge impact on development outcomes are agreed on; decisions are made about large-scale fund...
2003.08553
QnAMaker: Data to Bot in 2 Minutes
Having a bot for seamless conversations is a much-desired feature that products and services today seek for their websites and mobile apps. These bots help reduce traffic received by human support significantly by handling frequent and directly answerable known questions. Many such services have huge reference document...
{ "section_name": [ "Introduction", "System description ::: Architecture", "System description ::: Bot Development Process", "System description ::: Extraction", "System description ::: Retrieval And Ranking", "System description ::: Retrieval And Ranking ::: Pre-Processing", "System descr...
{ "question": [ "What experiments do the authors present to validate their system?", "How does the conversation layer work?", "What components is the QnAMaker composed of?" ], "question_id": [ "fd0ef5a7b6f62d07776bf672579a99c67e61a568", "071bcb4b054215054f17db64bfd21f17fd9e1a80", "f399d5a8...
{ "caption": [ "Figure 1: Interactions between various components of QnaMaker, along with their scopes: server-side and client-side", "Table 1: Retrieval And Ranking Measurements", "Figure 2: QnAMaker Runtime Pipeline", "Figure 3: Active Learning Suggestions", "Figure 4: Multi-Turn Knowledge Base"...
Introduction QnAMaker aims to simplify the process of bot creation by extracting Question-Answer (QA) pairs from data given by users into a Knowledge Base (KB) and providing a conversational layer over it. KB here refers to one instance of azure search index, where the extracted QA are stored. Whenever a developer crea...
1909.09491
A simple discriminative training method for machine translation with large-scale features
Margin infused relaxed algorithms (MIRAs) dominate model tuning in statistical machine translation in the case of large scale features, but also they are famous for the complexity in implementation. We introduce a new method, which regards an N-best list as a permutation and minimizes the Plackett-Luce loss of ground-t...
{ "section_name": [ "Introduction", "Plackett-Luce Model", "Plackett-Luce Model in Statistical Machine Translation", "Plackett-Luce Model in Statistical Machine Translation ::: N-best Hypotheses Resample", "Evaluation", "Evaluation ::: Plackett-Luce Model for SMT Tuning", "Evaluation ::: P...
{ "question": [ "How they measure robustness in experiments?", "Is new method inferior in terms of robustness to MIRAs in experiments?", "What experiments with large-scale features are performed?" ], "question_id": [ "d28260b5565d9246831e8dbe594d4f6211b60237", "8670989ca39214eda6c1d1d272457a3f...
{ "caption": [ "Table 2: PL(k): Plackett-Luce model optimizing the ground-truth permutation with length k. The significant symbols (+ at 0.05 level) are compared with MERT. The bold font numbers signifies better results compared to M(1) system.", "Figure 1: PL(k) with 500 L-BFGS iterations, k=1,3,5,7,9,12,15 ...
Introduction Since Och BIBREF0 proposed minimum error rate training (MERT) to exactly optimize objective evaluation measures, MERT has become a standard model tuning technique in statistical machine translation (SMT). Though MERT performs better by improving its searching algorithm BIBREF1, BIBREF2, BIBREF3, BIBREF4, i...
2001.05284
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses
In a modern spoken language understanding (SLU) system, the natural language understanding (NLU) module takes interpretations of a speech from the automatic speech recognition (ASR) module as the input. The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and in...
{ "section_name": [ "Introduction", "Baseline, Oracle and Direct Models ::: Baseline and Oracle", "Baseline, Oracle and Direct Models ::: Direct Models", "Integration of N-BEST Hypotheses", "Integration of N-BEST Hypotheses ::: Hypothesized Text Concatenation", "Integration of N-BEST Hypothese...
{ "question": [ "Which ASR system(s) is used in this work?", "What are the series of simple models?", "Over which datasets/corpora is this work evaluated?" ], "question_id": [ "67131c15aceeb51ae1d3b2b8241c8750a19cca8e", "579a0603ec56fc2b4aa8566810041dbb0cd7b5e7", "c9c85eee41556c6993f40e428...
{ "caption": [ "Fig. 3: Integration of n-best hypotheses with two possible ways: 1) concatenate hypothesized text and 2) concatenate hypothesis embedding.", "Table 3: Micro and Macro F1 score for multi-class domain classification.", "Table 4: Performance comparison for the subset (∼ 19%) where ASR first b...
Introduction Currently, voice-controlled smart devices are widely used in multiple areas to fulfill various tasks, e.g. playing music, acquiring weather information and booking tickets. The SLU system employs several modules to enable the understanding of the semantics of the input speeches. When there is an incoming s...
1909.12140
DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and German
We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications. For this purpose, w...
{ "section_name": [ "Introduction", "System Description", "System Description ::: Split into Minimal Propositions", "System Description ::: Establish a Semantic Hierarchy", "System Description ::: Establish a Semantic Hierarchy ::: Constituency Type Classification.", "System Description ::: Es...
{ "question": [ "Is the semantic hierarchy representation used for any task?", "What are the corpora used for the task?", "Is the model evaluated?" ], "question_id": [ "f8281eb49be3e8ea0af735ad3bec955a5dedf5b3", "a5ee9b40a90a6deb154803bef0c71c2628acb571", "e286860c41a4f704a3a08e45183cb8b14...
{ "caption": [ "Figure 1: DISSIM’s browser-based user interface. The simplified output is displayed in the form of a directed graph where the split sentences are connected by arrows whose labels denote the semantic relationship that holds between a pair of simplified sentences and whose direction indicates their ...
Introduction We developed a syntactic text simplification (TS) approach that can be used as a preprocessing step to facilitate and improve the performance of a wide range of artificial intelligence (AI) tasks, such as Machine Translation, Information Extraction (IE) or Text Summarization. Since shorter sentences are ge...
1709.00947
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects
This paper describes a preliminary study for producing and distributing a large-scale database of embeddings from the Portuguese Twitter stream. We start by experimenting with a relatively small sample and focusing on three challenges: volume of training data, vocabulary size and intrinsic evaluation metrics. Using a s...
{ "section_name": [ "Introduction", "Related Work", "Our Neural Word Embedding Model", "Experimental Setup", "Training Data", "Metrics related with the Learning Process", "Tests and Gold-Standard Data for Intrinsic Evaluation", "Results and Analysis", "Intrinsic Evaluation", "F...
{ "question": [ "What new metrics are suggested to track progress?", "What intrinsic evaluation metrics are used?", "What experimental results suggest that using less than 50% of the available training examples might result in overfitting?" ], "question_id": [ "982979cb3c71770d8d7d2d1be8f92b66223d...
{ "caption": [ "Table 1. Number of 5-grams available for training for different sizes of target vocabulary |V |", "Table 2. Overall statistics for 12 combinations of models learned varying |V | and volume of training data. Results observed after 40 training epochs.", "Fig. 1. Continuous line represents lo...
Introduction Word embeddings have great practical importance since they can be used as pre-computed high-density features to ML models, significantly reducing the amount of training data required in a variety of NLP tasks. However, there are several inter-related challenges with computing and consistently distributing ...
1909.08859
Procedural Reasoning Networks for Understanding Multimodal Procedures
This paper addresses the problem of comprehending procedural commonsense knowledge. This is a challenging task as it requires identifying key entities, keeping track of their state changes, and understanding temporal and causal relations. Contrary to most of the previous work, in this study, we do not rely on strong in...
{ "section_name": [ "Introduction", "Visual Reasoning in RecipeQA", "Procedural Reasoning Networks", "Procedural Reasoning Networks ::: Input Module", "Procedural Reasoning Networks ::: Reasoning Module", "Procedural Reasoning Networks ::: Attention Module", "Procedural Reasoning Networks ...
{ "question": [ "What multimodality is available in the dataset?", "What are previously reported models?", "How better is accuracy of new model compared to previously reported models?" ], "question_id": [ "a883bb41449794e0a63b716d9766faea034eb359", "5d83b073635f5fd8cd1bdb1895d3f13406583fbd", ...
{ "caption": [ "Figure 1: A recipe for preparing a cheeseburger (adapted from the cooking instructions available at https: //www.instructables.com/id/In-N-Out-Double-Double-Cheeseburger-Copycat). Each basic ingredient (entity) is highlighted by a different color in the text and with bounding boxes on the accompan...
Introduction A great deal of commonsense knowledge about the world we live is procedural in nature and involves steps that show ways to achieve specific goals. Understanding and reasoning about procedural texts (e.g. cooking recipes, how-to guides, scientific processes) are very hard for machines as it demands modeling...
1908.08419
Active Learning for Chinese Word Segmentation in Medical Text
Electronic health records (EHRs) stored in hospital information systems completely reflect the patients' diagnosis and treatment processes, which are essential to clinical data mining. Chinese word segmentation (CWS) is a fundamental and important task for Chinese natural language processing. Currently, most state-of-t...
{ "section_name": [ "Introduction", "Chinese Word Segmentation", "Active Learning", "Active Learning for Chinese Word Segmentation", "CRF-based Word Segmenter", "Information Entropy Based Scoring Model", "Datasets", "Parameter Settings", "Experimental Results", "Conclusion and ...
{ "question": [ "How does the scoring model work?", "How does the active learning model work?", "Which neural network architectures are employed?" ], "question_id": [ "3c3cb51093b5fd163e87a773a857496a4ae71f03", "53a0763eff99a8148585ac642705637874be69d4", "0bfed6f9cfe93617c5195c848583e3945f...
{ "caption": [ "Fig. 1. The diagram of active learning for the Chinese word segmentation.", "Fig. 2. The architecture of the information entropy based scoring model, where ‘/’ represents candidate word separator, xi represents the one-hot encoding of the i-th character, cj represents the j-th character embedd...
Introduction Electronic health records (EHRs) systematically collect patients' clinical information, such as health profiles, histories of present illness, past medical histories, examination results and treatment plans BIBREF0 . By analyzing EHRs, many useful information, closely related to patients, can be discovered...
1703.05260
InScript: Narrative texts annotated with script information
This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. T...
{ "section_name": [ "Motivation", "Collection via Amazon M-Turk", "Data Statistics", "Annotation", "Annotation Schema", "Development of the Schema", "First Annotation Phase", "Modification of the Schema", "Special Cases", "Inter-Annotator Agreement", "Annotated Corpus Stati...
{ "question": [ "What are the key points in the role of script knowledge that can be studied?", "Did the annotators agreed and how much?", "How many subjects have been used to create the annotations?" ], "question_id": [ "352c081c93800df9654315e13a880d6387b91919", "18fbf9c08075e3b696237d22473c...
{ "caption": [ "Figure 1: An excerpt from a story on the TAKING A BATH script.", "Figure 2: Connecting DeScript and InScript: an example from the BAKING A CAKE scenario (InScript participant annotation is omitted for better readability).", "Table 1: Bath scenario template (labels added in the second phase...
Motivation A script is “a standardized sequence of events that describes some stereotypical human activity such as going to a restaurant or visiting a doctor” BIBREF0 . Script events describe an action/activity along with the involved participants. For example, in the script describing a visit to a restaurant, typical ...
1905.00563
Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications
Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on improving accuracy and overlook other aspects such as robustness and interpretability. In this paper, we propose adversarial modifications for li...
{ "section_name": [ "Introduction", "Background and Notation", "Completion Robustness and Interpretability via Adversarial Graph Edits ()", "Removing a fact ()", "Adding a new fact ()", "Challenges", "Efficiently Identifying the Modification", "First-order Approximation of Influence", ...
{ "question": [ "What datasets are used to evaluate this approach?", "How is this approach used to detect incorrect facts?", "Can this adversarial approach be used to directly improve model accuracy?" ], "question_id": [ "bc9c31b3ce8126d1d148b1025c66f270581fde10", "185841e979373808d99dccdade52...
{ "caption": [ "Figure 1: Completion Robustness and Interpretability via Adversarial Graph Edits (CRIAGE): Change in the graph structure that changes the prediction of the retrained model, where (a) is the original sub-graph of the KG, (b) removes a neighboring link of the target, resulting in a change in the pre...
Introduction Knowledge graphs (KG) play a critical role in many real-world applications such as search, structured data management, recommendations, and question answering. Since KGs often suffer from incompleteness and noise in their facts (links), a number of recent techniques have proposed models that embed each ent...
1808.05902
Learning Supervised Topic Models for Classification and Regression from Crowds
The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ...
{ "section_name": [ "Introduction", "Supervised topic models", "Learning from multiple annotators", "Classification model", "Proposed model", "Approximate inference", "Parameter estimation", "Stochastic variational inference", "Document classification", "Regression model", ...
{ "question": [ "what are the advantages of the proposed model?", "what are the state of the art approaches?", "what datasets were used?" ], "question_id": [ "330f2cdeab689670b68583fc4125f5c0b26615a8", "c87b2dd5c439d5e68841a705dd81323ec0d64c97", "f7789313a804e41fcbca906a4e5cf69039eeef9f" ...
{ "caption": [ "Fig. 1. Graphical representation of the proposed model for classification.", "TABLE 1 Correspondence Between Variational Parameters and the Original Parameters", "Fig. 3. Graphical representation of the proposed model for regression.", "Fig. 2. Example of four different annotators (rep...
Introduction Topic models, such as latent Dirichlet allocation (LDA), allow us to analyze large collections of documents by revealing their underlying themes, or topics, and how each document exhibits them BIBREF0 . Therefore, it is not surprising that topic models have become a standard tool in data analysis, with man...
2002.11893
CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restauran...
{ "section_name": [ "Introduction", "Related Work", "Data Collection", "Data Collection ::: Database Construction", "Data Collection ::: Goal Generation", "Data Collection ::: Dialogue Collection", "Data Collection ::: Dialogue Collection ::: User Side", "Data Collection ::: Dialogue C...
{ "question": [ "How was the dataset collected?", "What are the benchmark models?", "How was the corpus annotated?" ], "question_id": [ "2376c170c343e2305dac08ba5f5bda47c370357f", "0137ecebd84a03b224eb5ca51d189283abb5f6d9", "5f6fbd57cce47f20a0fda27d954543c00c4344c2" ], "nlp_background"...
{ "caption": [], "file": [] }
Introduction Recently, there have been a variety of task-oriented dialogue models thanks to the prosperity of neural architectures BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. However, the research is still largely limited by the availability of large-scale high-quality dialogue data. Many corpora have advance...
1910.07181
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
Pretraining deep contextualized representations using an unsupervised language modeling objective has led to large performance gains for a variety of NLP tasks. Despite this success, recent work by Schick and Schutze (2019) suggests that these architectures struggle to understand rare words. For context-independent wor...
{ "section_name": [ "Introduction", "Related Work", "Model ::: Form-Context Model", "Model ::: Bertram", "Model ::: Training", "Generation of Rare Word Datasets", "Generation of Rare Word Datasets ::: Dataset Splitting", "Generation of Rare Word Datasets ::: Baseline Training", "Ge...
{ "question": [ "What models other than standalone BERT is new model compared to?", "How much is representaton improved for rare/medum frequency words compared to standalone BERT and previous work?", "What are three downstream task datasets?", "What is dataset for word probing task?" ], "question_...
{ "caption": [ "Figure 1: Schematic representation of BERTRAM in the add-gated configuration processing the input word w = “washables” given a single context C1 = “other washables such as trousers . . .” (left) and given multiple contexts C = {C1, . . . , Cm} (right)", "Table 1: Results on WNLaMPro test for b...
Introduction As traditional word embedding algorithms BIBREF1 are known to struggle with rare words, several techniques for improving their representations have been proposed over the last few years. These approaches exploit either the contexts in which rare words occur BIBREF2, BIBREF3, BIBREF4, BIBREF5, their surface...
1902.00330
Joint Entity Linking with Deep Reinforcement Learning
Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models. First, most of them calculate the pairwise ...
{ "section_name": [ "Introduction", "Methodology", "Preliminaries", "Local Encoder", "Global Encoder", "Entity Selector", "Experiment", "Experiment Setup", "Comparing with Previous Work", "Discussion on different RLEL variants", "Case Study", "Related Work", "Entity...
{ "question": [ "How fast is the model compared to baselines?", "How big is the performance difference between this method and the baseline?", "What datasets used for evaluation?", "what are the mentioned cues?" ], "question_id": [ "9aca4b89e18ce659c905eccc78eda76af9f0072a", "b0376a7f67f15...
{ "caption": [ "Figure 1: Illustration of mentions in the free text and their candidate entities in the knowledge base. Solid black lines point to the correct target entities corresponding to the mentions and to the descriptions of these correct target entities. Solid red lines indicate the consistency between co...
Introduction Entity Linking (EL), which is also called Entity Disambiguation (ED), is the task of mapping mentions in text to corresponding entities in a given knowledge Base (KB). This task is an important and challenging stage in text understanding because mentions are usually ambiguous, i.e., different named entitie...
1909.00542
Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b
Task B Phase B of the 2019 BioASQ challenge focuses on biomedical question answering. Macquarie University's participation applies query-based multi-document extractive summarisation techniques to generate a multi-sentence answer given the question and the set of relevant snippets. In past participation we explored the...
{ "section_name": [ "Introduction", "Related Work", "Classification vs. Regression Experiments", "Deep Learning Models", "Reinforcement Learning", "Evaluation Correlation Analysis", "Submitted Runs", "Conclusions" ], "paragraphs": [ [ "The BioASQ Challenge includes a ques...
{ "question": [ "How did the author's work rank among other submissions on the challenge?", "What approaches without reinforcement learning have been tried?", "What classification approaches were experimented for this task?", "Did classification models perform better than previous regression one?" ]...
{ "caption": [ "Table 1. Summarisation techniques used in BioASQ 6b for the generation of ideal answers. The evaluation result is the human evaluation of the best run.", "Fig. 2. Architecture of the neural classification and regression systems. A matrix of pre-trained word embeddings (same pre-trained vectors...
Introduction The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question answering challenges where the aim is normally to give an exact answer, u...
1810.06743
Marrying Universal Dependencies and Universal Morphology
The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages - UD at the token level and UniMorph at the type level. As each corpus is built by different a...
{ "section_name": [ "Introduction", "Background: Morphological Inflection", "Two Schemata, Two Philosophies", "Universal Dependencies", "UniMorph", "Similarities in the annotation", "UD treebanks and UniMorph tables", "A Deterministic Conversion", "Experiments", "Intrinsic eval...
{ "question": [ "What are the main sources of recall errors in the mapping?", "Do they look for inconsistencies between different languages' annotations in UniMorph?", "Do they look for inconsistencies between different UD treebanks?", "Which languages do they validate on?" ], "question_id": [ ...
{ "caption": [ "Figure 1: Example of annotation disagreement in UD between two languages on translations of one phrase, reproduced from Malaviya et al. (2018). The final word in each, “refrescante”, is not inflected for gender: It has the same surface form whether masculine or feminine. Only in Portuguese, it is ...
Introduction The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are annotated according to its own cross-lingual schema, prescribing how features like gender or case...
1909.02764
Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning
The recognition of emotions by humans is a complex process which considers multiple interacting signals such as facial expressions and both prosody and semantic content of utterances. Commonly, research on automatic recognition of emotions is, with few exceptions, limited to one modality. We describe an in-car experime...
{ "section_name": [ "Introduction", "Related Work ::: Facial Expressions", "Related Work ::: Acoustic", "Related Work ::: Text", "Data set Collection", "Data set Collection ::: Study Setup and Design", "Data set Collection ::: Procedure", "Data set Collection ::: Data Analysis", "M...
{ "question": [ "Does the paper evaluate any adjustment to improve the predicion accuracy of face and audio features?", "How is face and audio data analysis evaluated?", "What is the baseline method for the task?", "What are the emotion detection tools used for audio and face input?" ], "question_...
{ "caption": [ "Figure 1: The setup of the driving simulator.", "Table 1: Examples for triggered interactions with translations to English. (D: Driver, A: Agent, Co: Co-Driver)", "Table 2: Examples from the collected data set (with translation to English). E: Emotion, IT: interaction type with agent (A) a...
Introduction Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted face), or a textual unit (e.g., a transcribed utterance, a sentence, or a Tweet). The set of emotions is ofte...
1905.11901
Revisiting Low-Resource Neural Machine Translation: A Case Study
It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results. In this paper, we re-assess the validity of these result...
{ "section_name": [ "Introduction", "Low-Resource Translation Quality Compared Across Systems", "Improving Low-Resource Neural Machine Translation", "Mainstream Improvements", "Language Representation", "Hyperparameter Tuning", "Lexical Model", "Data and Preprocessing", "PBSMT Base...
{ "question": [ "what amounts of size were used on german-english?", "what were their experimental results in the low-resource dataset?", "what are the methods they compare with in the korean-english dataset?", "what pitfalls are mentioned in the paper?" ], "question_id": [ "4547818a3bbb727c4b...
{ "caption": [ "Figure 4: Translations of the first sentence of the test set using NMT system trained on varying amounts of training data. Under low resource conditions, NMT produces fluent output unrelated to the input.", "Table 1: Training corpus size and subword vocabulary size for different subsets of IWS...
Introduction While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underperform phrase-based statistical machine translation (...
1912.01252
Facilitating on-line opinion dynamics by mining expressions of causation. The case of climate change debates on The Guardian
News website comment sections are spaces where potentially conflicting opinions and beliefs are voiced. Addressing questions of how to study such cultural and societal conflicts through technological means, the present article critically examines possibilities and limitations of machine-guided exploration and potential...
{ "section_name": [ "Introduction ::: Background", "Introduction ::: Objective", "Introduction ::: Data: the communicative setting of TheGuardian.com", "Mining opinions and beliefs from texts", "Mining opinions and beliefs from texts ::: Causal mapping methods and the climate change debate", "...
{ "question": [ "Does the paper report the results of previous models applied to the same tasks?", "How is the quality of the discussion evaluated?", "What is the technique used for text analysis and mining?", "What are the causal mapping methods employed?" ], "question_id": [ "5679fabeadf680e...
{ "caption": [ "Figure 1. Communicative setting of many online newspaper sites. The newspaper publishes articles on different topics and users can comment on these articles and previous comments.", "Figure 2. This is a global representation of the data produced by considering a 10 percent subsample of all the...
Introduction ::: Background Over the past two decades, the rise of social media and the digitization of news and discussion platforms have radically transformed how individuals and groups create, process and share news and information. As Alan Rusbridger, former-editor-in-chief of the newspaper The Guardian has it, the...
1912.13109
"Hinglish"Language -- Modeling a Messy Code-Mixed Language
With a sharp rise in fluency and users of "Hinglish" in linguistically diverse country, India, it has increasingly become important to analyze social content written in this language in platforms such as Twitter, Reddit, Facebook. This project focuses on using deep learning techniques to tackle a classification problem...
{ "section_name": [ "Introduction", "Introduction ::: Modeling challenges", "Related Work ::: Transfer learning based approaches", "Related Work ::: Hybrid models", "Dataset and Features", "Dataset and Features ::: Challenges", "Model Architecture", "Model Architecture ::: Loss functio...
{ "question": [ "What is the previous work's model?", "What dataset is used?", "How big is the dataset?", "How is the dataset collected?", "Was each text augmentation technique experimented individually?", "What models do previous work use?", "Does the dataset contain content from various ...
{ "caption": [ "Table 1: Annotated Data set", "Table 2: Examples in the dataset", "Table 3: Train-test split", "Figure 1: Deep learning network used for the modeling", "Figure 2: Results of various experiments" ], "file": [ "2-Table1-1.png", "3-Table2-1.png", "4-Table3-1.png", ...
Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, but is used in India, with Englis...
1911.03310
How Language-Neutral is Multilingual BERT?
Multilingual BERT (mBERT) provides sentence representations for 104 languages, which are useful for many multi-lingual tasks. Previous work probed the cross-linguality of mBERT using zero-shot transfer learning on morphological and syntactic tasks. We instead focus on the semantic properties of mBERT. We show that mBER...
{ "section_name": [ "Introduction", "Related Work", "Centering mBERT Representations", "Probing Tasks", "Probing Tasks ::: Language Identification.", "Probing Tasks ::: Language Similarity.", "Probing Tasks ::: Parallel Sentence Retrieval.", "Probing Tasks ::: Word Alignment.", "Pr...
{ "question": [ "How they demonstrate that language-neutral component is sufficiently general in terms of modeling semantics to allow high-accuracy word-alignment?", "Are language-specific and language-neutral components disjunctive?", "How they show that mBERT representations can be split into a language...
{ "caption": [ "Table 1: Accuracy of language identification, values from the best-scoring layers.", "Figure 1: Language centroids of the mean-pooled representations from the 8th layer of cased mBERT on a tSNE plot with highlighted language families.", "Table 2: V-Measure for hierarchical clustering of la...
Introduction Multilingual BERT (mBERT; BIBREF0) is gaining popularity as a contextual representation for various multilingual tasks, such as dependency parsing BIBREF1, BIBREF2, cross-lingual natural language inference (XNLI) or named-entity recognition (NER) BIBREF3, BIBREF4, BIBREF5. BIBREF3 present an exploratory p...
1907.12108
CAiRE: An End-to-End Empathetic Chatbot
In this paper, we present an end-to-end empathetic conversation agent CAiRE. Our system adapts TransferTransfo (Wolf et al., 2019) learning approach that fine-tunes a large-scale pre-trained language model with multi-task objectives: response language modeling, response prediction and dialogue emotion detection. We eva...
{ "section_name": [ "Introduction", "User Interface", "Scalable to Multiple Users", "Generative Conversational Model", "Active Learning of Ethical Values and Persona", "Conclusion" ], "paragraphs": [ [ "Empathetic chatbots are conversational agents that can understand user emotio...
{ "question": [ "What is the performance of their system?", "What evaluation metrics are used?", "What is the source of the dialogues?", "What pretrained LM is used?" ], "question_id": [ "b1ced2d6dcd1d7549be2594396cbda34da6c3bca", "f3be1a27df2e6ad12eed886a8cd2dfe09b9e2b30", "a45a86b6a0...
{ "caption": [ "Table 1: An example of the empathetic dialogue dataset. Two people are discussing a situation that happened to one of them, and that led to the experience of a given feeling.", "Figure 1: Fine-tuning schema for empathetic dialogues.", "Table 2: Comparison of different automatic metrics bet...
Introduction Empathetic chatbots are conversational agents that can understand user emotions and respond appropriately. Incorporating empathy into the dialogue system is essential to achieve better human-robot interaction because naturally, humans express and perceive emotion in natural language to increase their sense...
2004.03685
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretation? In this opinion piece we reflect on the current state of interpretability evaluation research. We call for more clearly differentiating...
{ "section_name": [ "Introduction", "Faithfulness vs. Plausibility", "Inherently Interpretable?", "Evaluation via Utility", "Guidelines for Evaluating Faithfulness", "Guidelines for Evaluating Faithfulness ::: Be explicit in what you evaluate.", "Guidelines for Evaluating Faithfulness ::: ...
{ "question": [ "What approaches they propose?", "What faithfulness criteria does they propose?", "Which are three assumptions in current approaches for defining faithfulness?", "Which are key points in guidelines for faithfulness evaluation?" ], "question_id": [ "eeaceee98ef1f6c971dac7b0b8930...
{ "caption": [], "file": [] }
Introduction Fueled by recent advances in deep-learning and language processing, NLP systems are increasingly being used for prediction and decision-making in many fields BIBREF0, including sensitive ones such as health, commerce and law BIBREF1. Unfortunately, these highly flexible and highly effective neural models a...
1808.03894
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often unclear how and why they actually work. In this paper, we take a step toward explaining such deep learning based models through a case study...
{ "section_name": [ "Introduction", "Task and Model", "Visualization of Attention and Gating", "Attention", "LSTM Gating Signals", "Conclusion" ], "paragraphs": [ [ "Deep learning has achieved tremendous success for many NLP tasks. However, unlike traditional methods that provide...
{ "question": [ "Did they use the state-of-the-art model to analyze the attention?", "What is the performance of their model?", "How many layers are there in their model?", "Did they compare with gradient-based methods?" ], "question_id": [ "aceac4ad16ffe1af0f01b465919b1d4422941a6b", "f707...
{ "caption": [ "Figure 1: Normalized attention and attention saliency visualization. Each column shows visualization of one sample. Top plots depict attention visualization and bottom ones represent attention saliency visualization. Predicted (the same as Gold) label of each sample is shown on top of each column....
Introduction Deep learning has achieved tremendous success for many NLP tasks. However, unlike traditional methods that provide optimized weights for human understandable features, the behavior of deep learning models is much harder to interpret. Due to the high dimensionality of word embeddings, and the complex, typic...
1703.04617
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions...
{ "section_name": [ "Introduction", "Related Work", "The Baseline Model", "Question Understanding and Adaptation", "Set-Up", "Results", "Conclusions" ], "paragraphs": [ [ "Enabling computers to understand given documents and answer questions about their content has recently a...
{ "question": [ "What MC abbreviate for?", "how much of improvement the adaptation model can get?", "what is the architecture of the baseline model?", "What is the exact performance on SQUAD?" ], "question_id": [ "a891039441e008f1fd0a227dbed003f76c140737", "73738e42d488b32c9db89ac8adefc754...
{ "caption": [ "Figure 1: A high level view of our basic model.", "Figure 2: The inference layer implemented with a residual network.", "Figure 3: The discriminative block for question discrimination and adaptation.", "Table 1: The official leaderboard of single models on SQuAD test set as we submitte...
Introduction Enabling computers to understand given documents and answer questions about their content has recently attracted intensive interest, including but not limited to the efforts as in BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . Many specific problems such as machine comprehension and question a...
1909.00578
SUM-QE: a BERT-based Summary Quality Estimation Model
We propose SumQE, a novel Quality Estimation model for summarization based on BERT. The model addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation, without involving comparison with human references. SumQE achieves very high correlations with human rat...
{ "section_name": [ "Introduction", "Related Work", "Datasets", "Methods ::: The Sum-QE Model", "Methods ::: The Sum-QE Model ::: Single-task (BERT-FT-S-1):", "Methods ::: The Sum-QE Model ::: Multi-task with one regressor (BERT-FT-M-1):", "Methods ::: The Sum-QE Model ::: Multi-task with ...
{ "question": [ "What are their correlation results?", "What dataset do they use?", "What simpler models do they look at?", "What linguistic quality aspects are addressed?" ], "question_id": [ "ff28d34d1aaa57e7ad553dba09fc924dc21dd728", "ae8354e67978b7c333094c36bf9d561ca0c2d286", "0234...
{ "caption": [ "Figure 1: SUM-QE rates summaries with respect to five linguistic qualities (Dang, 2006a). The datasets we use for tuning and evaluation contain human assigned scores (from 1 to 5) for each of these categories.", "Figure 2: Illustration of different flavors of the investigated neural QE methods...
Introduction Quality Estimation (QE) is a term used in machine translation (MT) to refer to methods that measure the quality of automatically translated text without relying on human references BIBREF0, BIBREF1. In this study, we address QE for summarization. Our proposed model, Sum-QE, successfully predicts linguistic...
1911.09419
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symm...
{ "section_name": [ "Introduction", "Related Work", "Related Work ::: Model Category", "Related Work ::: The Ways to Model Hierarchy Structures", "The Proposed HAKE", "The Proposed HAKE ::: Two Categories of Entities", "The Proposed HAKE ::: Hierarchy-Aware Knowledge Graph Embedding", ...
{ "question": [ "What benchmark datasets are used for the link prediction task?", "What are state-of-the art models for this task?", "How better does HAKE model peform than state-of-the-art methods?", "How are entities mapped onto polar coordinate system?" ], "question_id": [ "6852217163ea678f...
{ "caption": [ "Table 1: Details of several knowledge graph embedding models, where ◦ denotes the Hadamard product, f denotes a activation function, ∗ denotes 2D convolution, and ω denotes a filter in convolutional layers. ·̄ denotes conjugate for complex vectors in ComplEx model and 2D reshaping for real vectors...
Introduction Knowledge graphs are usually collections of factual triples—(head entity, relation, tail entity), which represent human knowledge in a structured way. In the past few years, we have witnessed the great achievement of knowledge graphs in many areas, such as natural language processing BIBREF0, question answ...
1910.11471
Machine Translation from Natural Language to Code using Long-Short Term Memory
Making computer programming language more understandable and easy for the human is a longstanding problem. From assembly language to present day’s object-oriented programming, concepts came to make programming easier so that a programmer can focus on the logic and the architecture rather than the code and language itse...
{ "section_name": [ "Introduction", "Problem Description", "Problem Description ::: Programming Language Diversity", "Problem Description ::: Human Language Factor", "Problem Description ::: NLP of statements", "Proposed Methodology", "Proposed Methodology ::: Statistical Machine Translati...
{ "question": [ "What additional techniques are incorporated?", "What dataset do they use?", "Do they compare to other models?", "What is the architecture of the system?", "How long are expressions in layman's language?", "What additional techniques could be incorporated to further improve acc...
{ "caption": [ "Fig. 1. Text-Code bi-lingual corpus", "Fig. 2. Neural training model architecture of Text-To-Code", "Fig. 3. Accuracy gain in progress of training the RNN" ], "file": [ "4-Figure1-1.png", "5-Figure2-1.png", "6-Figure3-1.png" ] }
Introduction Removing computer-human language barrier is an inevitable advancement researchers are thriving to achieve for decades. One of the stages of this advancement will be coding through natural human language instead of traditional programming language. On naturalness of computer programming D. Knuth said, “Let ...
1910.09399
A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis
Text-to-image synthesis refers to computational methods which translate human written textual descriptions, in the form of keywords or sentences, into images with similar semantic meaning to the text. In earlier research, image synthesis relied mainly on word to image correlation analysis combined with supervised metho...
{ "section_name": [ "Introduction", "Introduction ::: blackTraditional Learning Based Text-to-image Synthesis", "Introduction ::: GAN Based Text-to-image Synthesis", "Related Work", "Preliminaries and Frameworks", "Preliminaries and Frameworks ::: Generative Adversarial Neural Network", "P...
{ "question": [ "Is text-to-image synthesis trained is suppervized or unsuppervized manner?", "What challenges remain unresolved?", "What is the conclusion of comparison of proposed solution?", "What is typical GAN architecture for each text-to-image synhesis group?" ], "question_id": [ "e96ad...
{ "caption": [ "Figure 1. Early research on text-to-image synthesis (Zhu et al., 2007). The system uses correlation between keywords (or keyphrase) and images and identifies informative and “picturable” text units, then searches for the most likely image parts conditioned on the text, and eventually optimizes the...
Introduction “ (GANs), and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.” (2016) – Yann LeCun A picture is worth a thousand words! While written text provide efficient, effective, and concise ways for communication, visual content, such as images, i...
1904.05584
Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study
In this paper we study how different ways of combining character and word-level representations affect the quality of both final word and sentence representations. We provide strong empirical evidence that modeling characters improves the learned representations at the word and sentence levels, and that doing so is par...
{ "section_name": [ "Introduction", "Background", "Mapping Characters to Character-level Word Representations", "Combining Character and Word-level Representations", "Obtaining Sentence Representations", "Experimental Setup", "Datasets", "Word Similarity", "Word Frequencies and Gat...
{ "question": [ "Where do they employ feature-wise sigmoid gating?", "Which model architecture do they use to obtain representations?", "Which downstream sentence-level tasks do they evaluate on?", "Which similarity datasets do they use?" ], "question_id": [ "7fe48939ce341212c1d801095517dc552b...
{ "caption": [ "Figure 1: Character and Word-level combination methods.", "Table 1: Word-level evaluation results. Each value corresponds to average Pearson correlation of 7 identical models initialized with different random seeds. Correlations were scaled to the [−100; 100] range for easier reading. Bold val...
Introduction Incorporating sub-word structures like substrings, morphemes and characters to the creation of word representations significantly increases their quality as reflected both by intrinsic metrics and performance in a wide range of downstream tasks BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . The reason for this i...
1911.09886
Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text. There may be multiple relation tuples present in a text and they may share one or both entities among them. Extracting such relation tuples from a sentence is a difficult task and sharing of en...
{ "section_name": [ "Introduction", "Task Description", "Encoder-Decoder Architecture", "Encoder-Decoder Architecture ::: Embedding Layer & Encoder", "Encoder-Decoder Architecture ::: Word-level Decoder & Copy Mechanism", "Encoder-Decoder Architecture ::: Pointer Network-Based Decoder", "E...
{ "question": [ "Are there datasets with relation tuples annotated, how big are datasets available?", "Which one of two proposed approaches performed better in experiments?", "What is previous work authors reffer to?", "How higher are F1 scores compared to previous work?" ], "question_id": [ "...
{ "caption": [ "Table 1: Relation tuple representation for encoder-decoder models.", "Figure 1: The architecture of an encoder-decoder model (left) and a pointer network-based decoder block (right).", "Table 2: Statistics of train/test split of the two datasets.", "Table 4: Ablation of attention mecha...
Introduction Distantly-supervised information extraction systems extract relation tuples with a set of pre-defined relations from text. Traditionally, researchers BIBREF0, BIBREF1, BIBREF2 use pipeline approaches where a named entity recognition (NER) system is used to identify the entities in a sentence and then a cla...
1611.01400
Learning to Rank Scientific Documents from the Crowd
Finding related published articles is an important task in any science, but with the explosion of new work in the biomedical domain it has become especially challenging. Most existing methodologies use text similarity metrics to identify whether two articles are related or not. However biomedical knowledge discovery is...
{ "section_name": [ null, "Introduction", "Benchmark Datasets", "Learning to Rank", "Features", "Baseline Systems", "Evaluation Measures", "Forward Feature Selection", "Results", "Discussion", "Acknowledgments" ], "paragraphs": [ [ "[block]I.1em", "[bloc...
{ "question": [ "what were the baselines?", "what is the supervised model they developed?", "what is the size of this built corpus?", "what crowdsourcing platform is used?" ], "question_id": [ "d32b6ac003cfe6277f8c2eebc7540605a60a3904", "c10f38ee97ed80484c1a70b8ebba9b1fb149bc91", "3405...
{ "caption": [ "Figure 1: The basic pipeline of a learning-to-rank system. An initial set of results for a query is retrieved from a search engine, and then that subset is reranked. During the reranking phase new features may be extracted.", "Table 1: Results for the citation baselines. The number of times a ...
None [block]I.1em [block]i.1em Learning to Rank Scientific Documents from the CrowdLearning to Rank Scientific Documents from the Crowd -4 [1]1 Introduction The number of biomedical research papers published has increased dramatically in recent years. As of October, 2016, PubMed houses over 26 million citations,...
1808.05077
Exploiting Deep Learning for Persian Sentiment Analysis
The rise of social media is enabling people to freely express their opinions about products and services. The aim of sentiment analysis is to automatically determine subject's sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as topic, product, movie, news etc. Deep learning has recently...
{ "section_name": [ "Introduction", "Related Works", "Methodology and Experimental Results", "Conclusion", "Acknowledgment" ], "paragraphs": [ [ "In recent years, social media, forums, blogs and other forms of online communication tools have radically affected everyday life, especial...
{ "question": [ "Which deep learning model performed better?", "By how much did the results improve?", "What was their performance on the dataset?", "How large is the dataset?" ], "question_id": [ "1951cde612751410355610074c3c69cec94824c2", "4140d8b5a78aea985546aa1e323de12f63d24add", "...
{ "caption": [ "Fig. 1. Multilayer Perceptron", "Fig. 2. Autoencoder", "Fig. 3. Deep Convolutional Neural Network", "Table 1. Results: MLP vs. Autoencoder vs. Convolutional Neural Network" ], "file": [ "5-Figure1-1.png", "5-Figure2-1.png", "6-Figure3-1.png", "7-Table1-1.png" ] }
Introduction In recent years, social media, forums, blogs and other forms of online communication tools have radically affected everyday life, especially how people express their opinions and comments. The extraction of useful information (such as people's opinion about companies brand) from the huge amount of unstruct...
1807.03367
Talk the Walk: Navigating New York City through Grounded Dialogue
We introduce"Talk The Walk", the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a"guide"and a"tourist") that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location. The task and dataset, which are de...
{ "section_name": [ null, "Introduction", "Talk The Walk", "Task", "Data Collection", "Dataset Statistics", "Experiments", "Tourist Localization", "Model", "The Tourist", "The Guide", "Comparisons", "Results and Discussion", "Analysis of Localization Task", ...
{ "question": [ "Did the authors use crowdsourcing platforms?", "How was the dataset collected?", "What language do the agents talk in?", "What evaluation metrics did the authors look at?", "What data did they use?" ], "question_id": [ "0cd0755ac458c3bafbc70e4268c1e37b87b9721b", "c1ce6...
{ "caption": [ "Figure 1: Example of the Talk The Walk task: two agents, a “tourist” and a “guide”, interact with each other via natural language in order to have the tourist navigate towards the correct location. The guide has access to a map and knows the target location but not the tourist location, while the ...
None 0pt0.03.03 * 0pt0.030.03 * 0pt0.030.03 We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given ...
1907.02030
Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks
Factchecking has always been a part of the journalistic process. However with newsroom budgets shrinking it is coming under increasing pressure just as the amount of false information circulating is on the rise. We therefore propose a method to increase the efficiency of the factchecking process, using the latest devel...
{ "section_name": [ "Introduction", "Related Work", "Method", "Choosing an embedding", "Clustering Method", "Next Steps" ], "paragraphs": [ [ "In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers ...
{ "question": [ "Do the authors report results only on English data?", "How is the accuracy of the system measured?", "How is an incoming claim used to retrieve similar factchecked claims?", "What existing corpus is used for comparison in these experiments?", "What are the components in the factch...
{ "caption": [ "Table 1: Examples of claims taken from real articles.", "Table 2: Claim Detection Results.", "Figure 1: Analysis of Different Embeddings on the Quora Question Answering Dataset", "Table 3: Comparing Sentence Embeddings for Clustering News Claims." ], "file": [ "2-Table1-1.png",...
Introduction In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true information BIBREF2 . Due to renewed focus on finding ways to foster healthy pol...
1910.04601
RC-QED: Evaluating Natural Language Derivations in Multi-Hop Reading Comprehension
Recent studies revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This allows systems to "cheat" by employing simple heuristics to answer questions, e.g. by relying on semantic type consistency. This means that current datasets are not well-suite...
{ "section_name": [ "Introduction", "Task formulation: RC-QED ::: Input, output, and evaluation metrics", "Task formulation: RC-QED ::: RC-QED@!START@$^{\\rm E}$@!END@", "Data collection for RC-QED@!START@$^{\\rm E}$@!END@ ::: Crowdsourcing interface", "Data collection for RC-QED@!START@$^{\\rm E}...
{ "question": [ "What is the baseline?", "What dataset was used in the experiment?", "Did they use any crowdsourcing platform?", "How was the dataset annotated?", "What is the source of the proposed dataset?" ], "question_id": [ "b11ee27f3de7dd4a76a1f158dc13c2331af37d9f", "7aba5e448329...
{ "caption": [ "Figure 1: Overview of the proposed RC-QED task. Given a question and supporting documents, a system is required to give an answer and its derivation steps.", "Figure 2: Crowdsourcing interface: judgement task.", "Figure 3: Crowdsourcing interface: derivation task.", "Table 1: Distribut...
Introduction Reading comprehension (RC) has become a key benchmark for natural language understanding (NLU) systems and a large number of datasets are now available BIBREF0, BIBREF1, BIBREF2. However, these datasets suffer from annotation artifacts and other biases, which allow systems to “cheat”: Instead of learning t...
1912.05066
Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds
Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has been done to explore how reliable the opinions of the mass (crowd wisdom) in social network microblogs such as twitter are in predicting ou...
{ "section_name": [ "Introduction", "Related Work", "Data Set and Preprocessing ::: Data Collection", "Data Set and Preprocessing ::: Preprocessing", "Methodology ::: Procedure", "Methodology ::: Machine Learning Models", "Methodology ::: Machine Learning Models ::: Single-label Classifica...
{ "question": [ "How many label options are there in the multi-label task?", "What is the interannotator agreement of the crowd sourced users?", "Who are the experts?", "Who is the crowd in these experiments?", "How do you establish the ground truth of who won a debate?" ], "question_id": [ ...
{ "caption": [ "TABLE I: Debates chosen, listed in chronological order. A total of 10 debates were considered out of which 7 are Republican and 3 are Democratic.", "TABLE II: Statistics of the Data Collected: Debates", "Fig. 1: Histograms of Tweet Frequency vs. Debates and TV Viewers vs. Debates shown sid...
Introduction Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions and express sentiment on a vari...
1910.03891
Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Many recent works have demonstrated the benefits of knowledge graph embedding on knowledge graph completion task, such as relation extraction. However, we observe that: 1) existing me...
{ "section_name": [ "Introduction", "Related Work", "Problem Formulation", "Proposed Model", "Proposed Model ::: Overall Architecture", "Proposed Model ::: Attribute Embedding Layer", "Proposed Model ::: Embedding Propagation Layer", "Proposed Model ::: Output Layer and Training Detail...
{ "question": [ "How much better is performance of proposed method than state-of-the-art methods in experiments?", "What further analysis is done?", "What seven state-of-the-art methods are used for comparison?", "What three datasets are used to measure performance?", "How does KANE capture both h...
{ "caption": [ "Figure 1: Subgraph of a knowledge graph contains entities, relations and attributes.", "Figure 2: Illustration of the KANE architecture.", "Table 1: The statistics of datasets.", "Table 2: Entity classification results in accuracy. We run all models 10 times and report mean ± standard ...
Introduction In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The facts in KGs are usually encoded in the form of triples $(\textit {head entit...
1610.00879
A Computational Approach to Automatic Prediction of Drunk Texting
Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks. We introduce automatic drunk-texting prediction as the task of identifying whether a text was written when under the influence of alcohol. We experiment with tweets labeled using hashtags as distant supervision. Our classifier...
{ "section_name": [ "Introduction", "Motivation", "Definition and Challenges", "Dataset Creation", "Feature Design", "Evaluation", "Performance for Datasets 1 and 2", "Performance for Held-out Dataset H", "Error Analysis", "Conclusion & Future Work" ], "paragraphs": [ [...
{ "question": [ "Do they report results only on English data?", "Do the authors mention any confounds to their study?", "What baseline model is used?", "What stylistic features are used to detect drunk texts?", "Is the data acquired under distant supervision verified by humans at any stage?", ...
{ "caption": [ "Figure 1: Word cloud for drunk tweets", "Table 1: Our Feature Set for Drunk-texting Prediction", "Table 2: Performance of our features on Datasets 1 and 2", "Table 4: Cohen’s Kappa for three annotators (A1A3)", "Table 3: Top stylistic features for Datasets 1 and 2 obtained using Ch...
Introduction The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this paper, we introduce auto...
1704.05572
Answering Complex Questions Using Open Information Extraction
While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such ...
{ "section_name": [ "Introduction", "Related Work", "Tuple Inference Solver", "Tuple KB", "Tuple Selection", "Support Graph Search", "Experiments", "Results", "Error Analysis", "Conclusion", "Appendix: ILP Model Details", "Experiment Details", "Using curated tables ...
{ "question": [ "What corpus was the source of the OpenIE extractions?", "What is the accuracy of the proposed technique?", "Is an entity linking process used?", "Are the OpenIE extractions all triples?", "What method was used to generate the OpenIE extractions?", "Can the method answer multi-...
{ "caption": [ "Figure 1: An example support graph linking a question (top), two tuples from the KB (colored) and an answer option (nitrogen).", "Table 2: TUPLEINF is significantly better at structured reasoning than TABLEILP.9", "Table 1: High-level ILP constraints; we report results for ~w = (2, 4, 4, 4...
Introduction Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Automatically constructed open vocabulary (subject; predica...
1804.10686
An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
In this paper, we present Watasense, an unsupervised system for word sense disambiguation. Given a sentence, the system chooses the most relevant sense of each input word with respect to the semantic similarity between the given sentence and the synset constituting the sense of the target word. Watasense has two modes ...
{ "section_name": [ "Introduction", "Related Work", "Watasense, an Unsupervised System for Word Sense Disambiguation", "System Architecture", "User Interface", "Word Sense Disambiguation", "Evaluation", "Quality Measure", "Dataset", "Results", "Conclusion", "Acknowledge...
{ "question": [ "Do the authors offer any hypothesis about why the dense mode outperformed the sparse one?", "What evaluation is conducted?", "Which corpus of synsets are used?", "What measure of semantic similarity is used?" ], "question_id": [ "7d5ba230522df1890619dedcfb310160958223c1", ...
{ "caption": [ "Figure 1: A snapshot of the online demo, which is available at http://watasense.nlpub.org/ (in Russian).", "Figure 2: The UML class diagram of Watasense.", "Figure 3: The word sense disambiguation results with the word “experiments” selected. The tooltip shows its lemma “experiment”, the s...
Introduction Word sense disambiguation (WSD) is a natural language processing task of identifying the particular word senses of polysemous words used in a sentence. Recently, a lot of attention was paid to the problem of WSD for the Russian language BIBREF0 , BIBREF1 , BIBREF2 . This problem is especially difficult bec...
1707.03904
Quasar: Datasets for Question Answering by Search and Reading
We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. The Quasar-S dataset consists of 37000 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website...
{ "section_name": [ "Introduction", "Dataset Construction", "Question sets", "Context Retrieval", "Candidate solutions", "Postprocessing", "Metrics", "Human Evaluation", "Baseline Systems", "Results", "Conclusion", "Acknowledgments", "Quasar-S Relation Definitions",...
{ "question": [ "Which retrieval system was used for baselines?" ], "question_id": [ "dcb18516369c3cf9838e83168357aed6643ae1b8" ], "nlp_background": [ "five" ], "topic_background": [ "familiar" ], "paper_read": [ "somewhat" ], "search_query": [ "question" ], "question_w...
{ "caption": [ "Figure 1: Example short-document instances from QUASAR-S (top) and QUASAR-T (bottom)", "Figure 2: Cloze generation", "Table 1: Dataset Statistics. Single-Token refers to the questions whose answer is a single token (for QUASAR-S all answers come from a fixed vocabulary). Answer in Short (L...
Introduction Factoid Question Answering (QA) aims to extract answers, from an underlying knowledge source, to information seeking questions posed in natural language. Depending on the knowledge source available there are two main approaches for factoid QA. Structured sources, including Knowledge Bases (KBs) such as Fre...
1911.07228
Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models
In recent years, Vietnamese Named Entity Recognition (NER) systems have had a great breakthrough when using Deep Neural Network methods. This paper describes the primary errors of the state-of-the-art NER systems on Vietnamese language. After conducting experiments on BLSTM-CNN-CRF and BLSTM-CRF models with different w...
{ "section_name": [ "Introduction", "Related work", "Error-analysis method", "Data and model ::: Data sets", "Data and model ::: Pre-trained word Embeddings", "Data and model ::: Model", "Experiment and Results", "Experiment and Results ::: Error analysis on gold data", "Experiment...
{ "question": [ "What word embeddings were used?", "What type of errors were produced by the BLSTM-CNN-CRF system?", "How much better was the BLSTM-CNN-CRF than the BLSTM-CRF?" ], "question_id": [ "f46a907360d75ad566620e7f6bf7746497b6e4a9", "79d999bdf8a343ce5b2739db3833661a1deab742", "71d5...
{ "caption": [ "Fig. 1. Chart flow to analyze errors based on gold labels", "Fig. 2. Chart flow to analyze errors based on predicted labels", "Table 1. Number type of each tags in the corpus", "Table 2. F1 score of two models with different pre-trained word embeddings", "Table 3. Performances of L...
Introduction Named Entity Recognition (NER) is one of information extraction subtasks that is responsible for detecting entity elements from raw text and can determine the category in which the element belongs, these categories include the names of persons, organizations, locations, expressions of times, quantities, mo...
1603.07044
Recurrent Neural Network Encoder with Attention for Community Question Answering
We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechan...
{ "section_name": [ "Introduction", "Related Work", "Method", "LSTM Models", "Neural Attention", "Predicting Relationships of Object Pairs with an Attention Model", "Modeling Question-External Comments", "Experiments", "Preliminary Results", "Robust Parameter Initialization", ...
{ "question": [ "What supplemental tasks are used for multitask learning?", "Is the improvement actually coming from using an RNN?", "How much performance gap between their approach and the strong handcrafted method?", "What is a strong feature-based method?", "Did they experimnet in other languag...
{ "caption": [ "Figure 1: RNN encoder for related question/comment selection.", "Figure 2: Neural attention model for related question/comment selection.", "Figure 3: Joint learning for external comment selection.", "Figure 4: IR-based system and feature-rich based system.", "Table 2: The RNN enco...
Introduction Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumulated a large collection of question-comment threads generated by t...
1902.09314
Attentional Encoder Network for Targeted Sentiment Classification
Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term...
{ "section_name": [ "Introduction", "Related Work", "Proposed Methodology", "Embedding Layer", "Attentional Encoder Layer", "Target-specific Attention Layer", "Output Layer", "Regularization and Model Training", "Datasets and Experimental Settings", "Model Comparisons", "Ma...
{ "question": [ "Do they use multi-attention heads?", "How big is their model?", "How is their model different from BERT?" ], "question_id": [ "9bffc9a9c527e938b2a95ba60c483a916dbd1f6b", "8434974090491a3c00eed4f22a878f0b70970713", "b67420da975689e47d3ea1c12b601851018c4071" ], "nlp_back...
{ "caption": [ "Figure 1: Overall architecture of the proposed AEN.", "Table 1: Statistics of the datasets.", "Table 2: Main results. The results of baseline models are retrieved from published papers. Top 2 scores are in bold.", "Table 3: Model sizes. Memory footprints are evaluated on the Restaurant...
Introduction Targeted sentiment classification is a fine-grained sentiment analysis task, which aims at determining the sentiment polarities (e.g., negative, neutral, or positive) of a sentence over “opinion targets” that explicitly appear in the sentence. For example, given a sentence “I hated their service, but their...
1904.03339
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples
This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and tra...
{ "section_name": [ "Introduction", "Joint Encoders for Stable Suggestion Inference", "Experiments", "Conclusion", "Acknowledgement" ], "paragraphs": [ [ "Opinion mining BIBREF0 is a huge field that covers many NLP tasks ranging from sentiment analysis BIBREF1 , aspect extraction BIB...
{ "question": [ "What datasets were used?", "How did they do compared to other teams?" ], "question_id": [ "01d91d356568fca79e47873bd0541bd22ba66ec0", "37e45a3439b048a80c762418099a183b05772e6a" ], "nlp_background": [ "", "" ], "topic_background": [ "", "" ], "paper_read...
{ "caption": [ "Figure 1: The overall architecture of JESSI for Subtask B. The thinner arrows correspond to the forward propagations, while the thicker arrows correspond to the backward propagations, where gradient calculations are indicated. For Subtask A, a CNN encoder is used instead of the BiSRU encoder, and ...
Introduction Opinion mining BIBREF0 is a huge field that covers many NLP tasks ranging from sentiment analysis BIBREF1 , aspect extraction BIBREF2 , and opinion summarization BIBREF3 , among others. Despite the vast literature on opinion mining, the task on suggestion mining has given little attention. Suggestion minin...
1910.11769
DENS: A Dataset for Multi-class Emotion Analysis
We introduce a new dataset for multi-class emotion analysis from long-form narratives in English. The Dataset for Emotions of Narrative Sequences (DENS) was collected from both classic literature available on Project Gutenberg and modern online narratives available on Wattpad, annotated using Amazon Mechanical Turk. A ...
{ "section_name": [ "Introduction", "Background", "Dataset", "Dataset ::: Plutchik’s Wheel of Emotions", "Dataset ::: Passage Selection", "Dataset ::: Mechanical Turk (MTurk)", "Dataset ::: Dataset Statistics", "Benchmarks", "Benchmarks ::: Bag-of-Words-based Benchmarks", "Benc...
{ "question": [ "Which tested technique was the worst performer?", "How many emotions do they look at?", "What are the baseline benchmarks?", "What is the size of this dataset?", "How many annotators were there?" ], "question_id": [ "a4e66e842be1438e5cd8d7cb2a2c589f494aee27", "cb78e280...
{ "caption": [ "Figure 1: Plutchik’s wheel of emotions (Wikimedia, 2011)", "Table 1: Genre distribution of the modern narratives", "Table 4: Benchmark results (averaged 5-fold cross validation)", "Table 2: Dataset label distribution" ], "file": [ "2-Figure1-1.png", "3-Table1-1.png", "4...
Introduction Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms. Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweets BIBREF1, BIBREF2. These datasets are oft...
1702.06378
Multitask Learning with CTC and Segmental CRF for Speech Recognition
Segmental conditional random fields (SCRFs) and connectionist temporal classification (CTC) are two sequence labeling methods used for end-to-end training of speech recognition models. Both models define a transcription probability by marginalizing decisions about latent segmentation alternatives to derive a sequence p...
{ "section_name": [ "Introduction", "Segmental Conditional Random Fields", "Feature Function and Acoustic Embedding", "Loss Function", "Connectionist Temporal Classification ", "Joint Training Loss", "Experiments", "Baseline Results", "Multitask Learning Results", "Conclusion",...
{ "question": [ "Can SCRF be used to pretrain the model?" ], "question_id": [ "aecb485ea7d501094e50ad022ade4f0c93088d80" ], "nlp_background": [ "" ], "topic_background": [ "familiar" ], "paper_read": [ "no" ], "search_query": [ "pretrain" ], "question_writer": [ "50...
{ "caption": [ "Figure 1: A Segmental RNN with the context aware embedding. The acoustic segmental embedding vector is composed by the hidden states from the RNN encoder corresponding to the beginning and end time tags.", "Table 1: Phone error rates of baseline CTC and SRNN models.", "Figure 2: Convergenc...
Introduction State-of-the-art speech recognition accuracy has significantly improved over the past few years since the application of deep neural networks BIBREF0 , BIBREF1 . Recently, it has been shown that with the application of both neural network acoustic model and language model, an automatic speech recognizer ca...
1903.03467
Filling Gender&Number Gaps in Neural Machine Translation with Black-box Context Injection
When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must"guess"this missing information, often leading to incorrect translations in the given context. We propose a black-box approach for injecting the missing informatio...
{ "section_name": [ "Introduction", "Morphological Ambiguity in Translation", "Black-Box Knowledge Injection", "Experiments & Results", "Quantitative Results", "Qualitative Results", "Comparison to vanmassenhove-hardmeier-way:2018:EMNLP", "Other Languages", "Related Work", "Con...
{ "question": [ "What conclusions are drawn from the syntactic analysis?", "What type of syntactic analysis is performed?", "How is it demonstrated that the correct gender and number information is injected using this system?", "Which neural machine translation system is used?", "What are the comp...
{ "caption": [ "Table 1: BLEU results on the Silverman dataset", "Figure 1: Gender inflection statistics for verbs governed by first-person pronouns.", "Table 2: Comparison of our approach (using Google Translate) to Vanmassenhove et al. (2018) on their English-French gender corpus.", "Table 3: Exampl...
Introduction A common way for marking information about gender, number, and case in language is morphology, or the structure of a given word in the language. However, different languages mark such information in different ways – for example, in some languages gender may be marked on the head word of a syntactic depende...
1807.00868
Exploring End-to-End Techniques for Low-Resource Speech Recognition
In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 hours). We have investigated different neural network architectures performance, including fully-convolutional, recurrent and ResNet with GRU. Different features and normalization...
{ "section_name": [ "Introduction", "Related work", "Basic setup", "Experiments with architecture", "Loss modification: segmenting during training", "Using different features", "Varying model size and number of layers", "Training the best model", "Conclusions and future work", ...
{ "question": [ "What normalization techniques are mentioned?", "What features do they experiment with?", "Which architecture is their best model?", "What kind of spontaneous speech is used?" ], "question_id": [ "d20d6c8ecd7cb0126479305d27deb0c8b642b09f", "11e6b79f1f48ddc6c580c4d0a3cb9bcb4...
{ "caption": [ "Fig. 1: Architectures", "Table 1: Baseline models trained with CTC-loss", "Table 2: Models trained with CTC and proposed CTC modification", "Table 3: 6-layers bLSTM trained using different features and normalization", "Table 4: Comparison of bLSTM models with different number of hi...
Introduction Although development of the first speech recognition systems began half a century ago, there has been a significant increase of the accuracy of ASR systems and number of their applications for the recent ten years, even for low-resource languages BIBREF0 , BIBREF1 . This is mainly due to widespread applyi...
1909.13375
Tag-based Multi-Span Extraction in Reading Comprehension
With models reaching human performance on many popular reading comprehension datasets in recent years, a new dataset, DROP, introduced questions that were expected to present a harder challenge for reading comprehension models. Among these new types of questions were "multi-span questions", questions whose answers cons...
{ "section_name": [ "Introduction", "Related Work", "Model", "Model ::: NABERT+", "Model ::: NABERT+ ::: Heads Shared with NABERT+", "Model ::: Multi-Span Head", "Model ::: Objective and Training", "Model ::: Objective and Training ::: Multi-Span Head Training Objective", "Model ::...
{ "question": [ "What approach did previous models use for multi-span questions?", "How they use sequence tagging to answer multi-span questions?", "What is difference in peformance between proposed model and state-of-the art on other question types?", "What is the performance of proposed model on ent...
{ "caption": [ "Table 1. Examples of faulty answers for multi-span questions in the training dataset, with their perfect clean answers, and answers generated by our cleaning method", "Table 2. Performance of different models on DROP’s development set in terms of Exact Match (EM) and F1.", "Table 3. Compar...
Introduction The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. With models reaching human performance on the popular SQuAD dataset BIBREF0, and with much of the most popular r...
1909.00430
Transfer Learning Between Related Tasks Using Expected Label Proportions
Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions. We propose a novel applica...
{ "section_name": [ "Introduction", "Lightly Supervised Learning", "Expectation Regularization (XR)", "Aspect-based Sentiment Classification", "Transfer-training between related tasks with XR", "Stochastic Batched Training for Deep XR", "Application to Aspect-based Sentiment", "Relatin...
{ "question": [ "How much more data does the model trained using XR loss have access to, compared to the fully supervised model?", "Does the system trained only using XR loss outperform the fully supervised neural system?", "How accurate is the aspect based sentiment classifier trained only using the XR l...
{ "caption": [ "Figure 1: Illustration of the algorithm. Cs is applied to Du resulting in ỹ for each sentence, Uj is built according with the fragments of the same labelled sentences, the probabilities for each fragment in Uj are summed and normalized, the XR loss in equation (4) is calculated and the network is...
Introduction Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work well. However, many tasks require manual annotations which are relatively hard to obtain at scale. ...
1910.11493
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years' inflection tasks by examining trans...
{ "section_name": [ "Introduction", "Tasks and Evaluation ::: Task 1: Cross-lingual transfer for morphological inflection", "Tasks and Evaluation ::: Task 1: Cross-lingual transfer for morphological inflection ::: Example", "Tasks and Evaluation ::: Task 1: Cross-lingual transfer for morphological inf...
{ "question": [ "What were the non-neural baselines used for the task?" ], "question_id": [ "b65b1c366c8bcf544f1be5710ae1efc6d2b1e2f1" ], "nlp_background": [ "two" ], "topic_background": [ "unfamiliar" ], "paper_read": [ "no" ], "search_query": [ "morphology" ], "questi...
{ "caption": [ "Table 1: Sample language pair and data format for Task 1", "Table 2: Task 1 Team Scores, averaged across all Languages; * indicates submissions were only applied to a subset of languages, making scores incomparable. † indicates that additional resources were used for training.", "Table 3: ...
Introduction While producing a sentence, humans combine various types of knowledge to produce fluent output—various shades of meaning are expressed through word selection and tone, while the language is made to conform to underlying structural rules via syntax and morphology. Native speakers are often quick to identify...
1910.00912
Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU
We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures). The architecture is a hierarchy of self-at...
{ "section_name": [ "Introduction", "Introduction ::: Cross-domain NLU", "Introduction ::: Multi-task NLU", "Introduction ::: Multi-dialogue act and -intent NLU", "Related Work", "Jointly parsing dialogue acts and frame-like structures", "Jointly parsing dialogue acts and frame-like struct...
{ "question": [ "Which publicly available NLU dataset is used?", "What metrics other than entity tagging are compared?" ], "question_id": [ "bd3ccb63fd8ce5575338d7332e96def7a3fabad6", "7c794fa0b2818d354ca666969107818a2ffdda0c" ], "nlp_background": [ "zero", "zero" ], "topic_backgro...
{ "caption": [ "Figure 1: Dialogue Acts (DAs), Frames (FRs – here semantic frames) and Arguments (ARs – here frame elements) IOB2 tagging for the sentence Where can I find Starbucks?", "Figure 2: HERMIT Network topology", "Table 2: Statistics of the ROMULUS dataset.", "Table 1: Statistics of the NLU-B...
Introduction Research in Conversational AI (also known as Spoken Dialogue Systems) has applications ranging from home devices to robotics, and has a growing presence in industry. A key problem in real-world Dialogue Systems is Natural Language Understanding (NLU) – the process of extracting structured representations o...
1908.10449
Interactive Machine Comprehension with Information Seeking Agents
Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary inf...
{ "section_name": [ "Introduction", "Related Works", "iMRC: Making MRC Interactive", "iMRC: Making MRC Interactive ::: Interactive MRC as a POMDP", "iMRC: Making MRC Interactive ::: Action Space", "iMRC: Making MRC Interactive ::: Query Types", "iMRC: Making MRC Interactive ::: Evaluation ...
{ "question": [ "Do they provide decision sequences as supervision while training models?", "What are the models evaluated on?", "How do they train models in this setup?", "What commands does their setup provide to models seeking information?" ], "question_id": [ "1ef5fc4473105f1c72b4d35cf93d3...
{ "caption": [ "Table 1: Examples of interactive machine reading comprehension behavior. In the upper example, the agent has no memory of past observations, and thus it answers questions only with observation string at current step. In the lower example, the agent is able to use its memory to find answers.", ...
Introduction Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these datasets require an MRC model to read through a document to answer a question abo...
1910.03814
Exploring Hate Speech Detection in Multimodal Publications
In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimo...
{ "section_name": [ "Introduction", "Related Work ::: Hate Speech Detection", "Related Work ::: Visual and Textual Data Fusion", "The MMHS150K dataset", "The MMHS150K dataset ::: Tweets Gathering", "The MMHS150K dataset ::: Textual Image Filtering", "The MMHS150K dataset ::: Annotation", ...
{ "question": [ "What models do they propose?", "Are all tweets in English?", "How large is the dataset?", "What is the results of multimodal compared to unimodal models?", "What is author's opinion on why current multimodal models cannot outperform models analyzing only text?", "What metrics ...
{ "caption": [ "Figure 1. Tweets from MMHS150K where the visual information adds relevant context for the hate speech detection task.", "Figure 2. Percentage of tweets per class in MMHS150K.", "Figure 3. Percentage of hate and not hate tweets for top keywords of MMHS150K.", "Figure 4. FCM architecture...
Introduction Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (or any form of expre...
1701.00185
Self-Taught Convolutional Neural Networks for Short Text Clustering
Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep tex...
{ "section_name": [ "Introduction", "Related Work", "Short Text Clustering", "Deep Neural Networks", "Methodology", "Deep Convolutional Neural Networks", "Unsupervised Dimensionality Reduction", "Learning", "K-means for Clustering", "Datasets", "Pre-trained Word Vectors", ...
{ "question": [ "What were the evaluation metrics used?", "What were their performance results?", "By how much did they outperform the other methods?", "Which popular clustering methods did they experiment with?", "What datasets did they use?" ], "question_id": [ "62a6382157d5f9c1dce6e6c24...
{ "caption": [ "Figure 1: The architecture of our proposed STC2 framework for short text clustering. Solid and hollow arrows represent forward and backward propagation directions of features and gradients respectively. The STC2 framework consist of deep convolutional neural network (CNN), unsupervised dimensional...
Introduction Short text clustering is of great importance due to its various applications, such as user profiling BIBREF0 and recommendation BIBREF1 , for nowaday's social media dataset emerged day by day. However, short text clustering has the data sparsity problem and most words only occur once in each short text BIB...
1912.00871
Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representations
Constructing accurate and automatic solvers of math word problems has proven to be quite challenging. Prior attempts using machine learning have been trained on corpora specific to math word problems to produce arithmetic expressions in infix notation before answer computation. We find that custom-built neural networks...
{ "section_name": [ "Introduction", "Related Work", "Approach", "Approach ::: Data", "Approach ::: Representation Conversion", "Approach ::: Pre-training", "Approach ::: Method: Training and Testing", "Approach ::: Method: Training and Testing ::: Objective Function", "Approach :::...
{ "question": [ "Does pre-training on general text corpus improve performance?", "What neural configurations are explored?", "Are the Transformers masked?", "How is this problem evaluated?", "What datasets do they use?" ], "question_id": [ "3f6610d1d68c62eddc2150c460bf1b48a064e5e6", "4...
{ "caption": [ "TABLE I BLEU-2 COMPARISON FOR EXPERIMENT 1.", "TABLE II SUMMARY OF BLEU SCORES FROM TABLE I.", "TABLE III TEST RESULTS FOR EXPERIMENT 2 (* DENOTES AVERAGES ON PRESENT VALUES ONLY).", "TABLE IV SUMMARY OF ACCURACIES FROM TABLE III." ], "file": [ "4-TableI-1.png", "4-TableII-...
Introduction Students are exposed to simple arithmetic word problems starting in elementary school, and most become proficient in solving them at a young age. Automatic solvers of such problems could potentially help educators, as well as become an integral part of general question answering services. However, it has b...
1912.03234
What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant
A considerable part of the success experienced by Voice-controlled virtual assistants (VVA) is due to the emotional and personalized experience they deliver, with humor being a key component in providing an engaging interaction. In this paper we describe methods used to improve the joke skill of a VVA through personali...
{ "section_name": [ "Introduction", "Method ::: Labelling Strategies", "Method ::: Features", "Method ::: NLP-based: LR-Model", "Method ::: Deep-Learning-based: DL-Models", "Validation", "Validation ::: Online Results: A/B Testing", "Validation ::: Offline Results", "Conclusions an...
{ "question": [ "What evaluation metrics were used?", "Where did the real production data come from?", "What feedback labels are used?" ], "question_id": [ "57e783f00f594e08e43a31939aedb235c9d5a102", "9646fa1abbe3102a0364f84e0a55d107d45c97f0", "29983f4bc8a5513a198755e474361deee93d4ab6" ]...
{ "caption": [ "Table 1: Example of labelling strategies: five-minute reuse (label 1) and 1-day return (label 2)", "Table 2: Examples of features within each category", "Figure 1: Architecture of the transformer-based model", "Table 3: Hyperparameter values tuned over, LR (top) and DL models (bottom)"...
Introduction Voice-controlled virtual assistants (VVA) such as Siri and Alexa have experienced an exponential growth in terms of number of users and provided capabilities. They are used by millions for a variety of tasks including shopping, playing music, and even telling jokes. Arguably, their success is due in part t...
1911.11750
A Measure of Similarity in Textual Data Using Spearman's Rank Correlation Coefficient
In the last decade, many diverse advances have occurred in the field of information extraction from data. Information extraction in its simplest form takes place in computing environments, where structured data can be extracted through a series of queries. The continuous expansion of quantities of data have therefore p...
{ "section_name": [ "Introduction", "Background", "Background ::: Document Representation", "Background ::: Measures of Similarity", "Related Work", "The Spearman's Rank Correlation Coefficient Similarity Measure", "The Spearman's Rank Correlation Coefficient Similarity Measure ::: Spearma...
{ "question": [ "What representations for textual documents do they use?", "Which dataset(s) do they use?", "How do they evaluate knowledge extraction performance?" ], "question_id": [ "6c0f97807cd83a94a4d26040286c6f89c4a0f8e0", "13ca4bf76565564c8ec3238c0cbfacb0b41e14d2", "70797f66d96aa163...
{ "caption": [ "TABLE II. A COMPARISON BETWEEN THE MEASURES CS, SRCC, PCC", "Fig. 1. A visual comparison of similarities produced by CS, SRCC and PCC", "Fig. 2. The association between documents" ], "file": [ "3-TableII-1.png", "4-Figure1-1.png", "4-Figure2-1.png" ] }
Introduction Over the past few years, the term big data has become an important key point for research into data mining and information retrieval. Through the years, the quantity of data managed across enterprises has evolved from a simple and imperceptible task to an extent to which it has become the central performan...
1911.03894
CamemBERT: a Tasty French Language Model
Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models—in all languages except English—very limited. Aiming to addre...
{ "section_name": [ "Introduction", "Related Work ::: From non-contextual to contextual word embeddings", "Related Work ::: Non-contextual word embeddings for languages other than English", "Related Work ::: Contextualised models for languages other than English", "CamemBERT", "CamemBERT ::: A...
{ "question": [ "What is CamemBERT trained on?", "Which tasks does CamemBERT not improve on?", "What is the state of the art?", "How much better was results of CamemBERT than previous results on these tasks?", "Was CamemBERT compared against multilingual BERT on these tasks?", "How long was Ca...
{ "caption": [ "Table 1: Sizes in Number of tokens, words and phrases of the 4 treebanks used in the evaluations of POS-tagging and dependency parsing.", "Table 2: Final POS and dependency parsing scores of CamemBERT and mBERT (fine-tuned in the exact same conditions as CamemBERT), UDify as reported in the or...
Introduction Pretrained word representations have a long history in Natural Language Processing (NLP), from non-neural methods BIBREF0, BIBREF1, BIBREF2 to neural word embeddings BIBREF3, BIBREF4 and to contextualised representations BIBREF5, BIBREF6. Approaches shifted more recently from using these representations as...
2001.09899
Vocabulary-based Method for Quantifying Controversy in Social Media
Identifying controversial topics is not only interesting from a social point of view, it also enables the application of methods to avoid the information segregation, creating better discussion contexts and reaching agreements in the best cases. In this paper we develop a systematic method for controversy detection bas...
{ "section_name": [ "Introduction", "Related work", "Method", "Experiments", "Experiments ::: Topic definition", "Experiments ::: Datasets", "Experiments ::: Results", "Discussions", "Discussions ::: Limitations", "Discussions ::: Conclusions", "Details on the discussions" ...
{ "question": [ "What are the state of the art measures?", "What controversial topics are experimented with?", "What datasets did they use?", "What social media platform is observed?", "How many languages do they experiment with?" ], "question_id": [ "bf25a202ac713a34e09bf599b3601058d9cace...
{ "caption": [ "Fig. 1", "Fig. 2", "Table 1: Datasets statistics, the top group represent controversial topics, while the bottom one represent non-controversial ones" ], "file": [ "8-Figure1-1.png", "11-Figure2-1.png", "15-Table1-1.png" ] }
Introduction Controversy is a phenomenom with a high impact at various levels. It has been broadly studied from the perspective of different disciplines, ranging from the seminal analysis of the conflicts within the members of a karate club BIBREF0 to political issues in modern times BIBREF1, BIBREF2. The irruption of ...
1710.01492
Semantic Sentiment Analysis of Twitter Data
Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions...
{ "section_name": [ "Synonyms", "Glossary", "Definition", "Introduction", "Key Points", "Historical Background", "Variants of the Task at SemEval", "Features and Learning", "Sentiment Polarity Lexicons", "Key Applications", "Future Directions", "Cross-References", "...
{ "question": [ "What is the current SOTA for sentiment analysis on Twitter at the time of writing?", "What difficulties does sentiment analysis on Twitter have, compared to sentiment analysis in other domains?", "What are the metrics to evaluate sentiment analysis on Twitter?" ], "question_id": [ ...
{ "caption": [], "file": [] }
Synonyms Microblog sentiment analysis; Twitter opinion mining Glossary Sentiment Analysis: This is text analysis aiming to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a piece of text. Definition Sentiment analysis on Twitter is the use of natural la...
1912.01673
COSTRA 1.0: A Dataset of Complex Sentence Transformations
COSTRA 1.0 is a dataset of Czech complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. ::: The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such ...
{ "section_name": [ "Introduction", "Background", "Annotation", "Annotation ::: First Round: Collecting Ideas", "Annotation ::: Second Round: Collecting Data ::: Sentence Transformations", "Annotation ::: Second Round: Collecting Data ::: Seed Data", "Annotation ::: Second Round: Collectin...
{ "question": [ "How many sentence transformations on average are available per unique sentence in dataset?", "What annotations are available in the dataset?", "How are possible sentence transformations represented in dataset, as new sentences?", "What are all 15 types of modifications ilustrated in t...
{ "caption": [ "Table 1: Examples of transformations given to annotators for the source sentence Several hunters slept on a clearing. The third column shows how many of all the transformation suggestions collected in the first round closely mimic the particular example. The number is approximate as annotators typ...
Introduction Vector representations are becoming truly essential in majority of natural language processing tasks. Word embeddings became widely popular with the introduction of word2vec BIBREF0 and GloVe BIBREF1 and their properties have been analyzed in length from various aspects. Studies of word embeddings range f...
1909.12231
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization
Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on hand-crafted features, which are domain-dependent and hard to cra...
{ "section_name": [ "Introduction", "Method", "Method ::: Sentence Semantic Relation Graph", "Method ::: Sentence Encoder", "Method ::: Graph Convolutional Network", "Method ::: Saliency Estimation", "Method ::: Training", "Method ::: Summary Generation Process", "Experiments ::: D...
{ "question": [ "How big is dataset domain-specific embedding are trained on?", "How big is unrelated corpus universal embedding is traned on?", "How better are state-of-the-art results than this model? " ], "question_id": [ "1a7d28c25bb7e7202230e1b70a885a46dac8a384", "6bc45d4f9086729451923906...
{ "caption": [ "Figure 1: Overview of SemSentSum. This illustration includes two documents in the collection, where the first one has three sentences and the second two. A sentence semantic relation graph is firstly built and each sentence node is processed by an encoder network at the same time. Thereafter, a si...
Introduction Today's increasing flood of information on the web creates a need for automated multi-document summarization systems that produce high quality summaries. However, producing summaries in a multi-document setting is difficult, as the language used to display the same information in a sentence can vary signif...
1706.08032
A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking
This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for ...
{ "section_name": [ "Introduction", "Basic idea", "Data Preparation", "Preprocessing", "Semantic Rules (SR)", "Representation Levels", "Deep Learning Module", "Regularization", " Experimental setups", "Experimental results", "Analysis", "Conclusions" ], "paragraphs"...
{ "question": [ "What were their results on the three datasets?", "What was the baseline?", "Which datasets did they use?", "Are results reported only on English datasets?", "Which three Twitter sentiment classification datasets are used for experiments?", "What semantic rules are proposed?" ...
{ "caption": [ "Figure 1. The overview of a deep learning system.", "Table II THE NUMBER OF TWEETS ARE PROCESSED BY USING SEMANTIC RULES", "Table I SEMANTIC RULES [12]", "Figure 2. Deep Convolutional Neural Network (DeepCNN) for the sequence of character embeddings of a word. For example with 1 region...
Introduction Twitter sentiment classification have intensively researched in recent years BIBREF0 BIBREF1 . Different approaches were developed for Twitter sentiment classification by using machine learning such as Support Vector Machine (SVM) with rule-based features BIBREF2 and the combination of SVMs and Naive Bayes...
1811.01399
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
Knowledge graph embedding aims at modeling entities and relations with low-dimensional vectors. Most previous methods require that all entities should be seen during training, which is unpractical for real-world knowledge graphs with new entities emerging on a daily basis. Recent efforts on this issue suggest training ...
{ "section_name": [ "Introduction", "Transductive Embedding Models", "Inductive Embedding Models", "Notations", "Framework", "Logic Attention Network", "Incorporating Neighborhood Attention", "Training Objective", "Experimental Configurations", "Data Construction", "Experim...
{ "question": [ "Which knowledge graph completion tasks do they experiment with?", "Apart from using desired properties, do they evaluate their LAN approach in some other way?", "Do they evaluate existing methods in terms of desired properties?" ], "question_id": [ "69a7a6675c59a4c5fb70006523b9fe0...
{ "caption": [ "Figure 1: A motivating example of emerging KG entities. Dotted circles and arrows represent the existing KG while solid ones are brought by the emerging entity.", "Figure 2: The encoder-decoder framework.", "Table 1: Statistics of the processed FB15K dataset.", "Table 2: Evaluation acc...
Introduction Knowledge graphs (KGs) such as Freebase BIBREF0 , DBpedia BIBREF1 , and YAGO BIBREF2 play a critical role in various NLP tasks, including question answering BIBREF3 , information retrieval BIBREF4 , and personalized recommendation BIBREF5 . A typical KG consists of numerous facts about a predefined set of ...
1909.00124
Learning with Noisy Labels for Sentence-level Sentiment Classification
Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the model may perform poorly. This paper studies the problem of learning with noisy labels for sentence-level sentiment classification. We prop...
{ "section_name": [ "Introduction", "Related Work", "Proposed Model", "Experiments", "Conclusions", "Acknowledgments" ], "paragraphs": [ [ "It is well known that sentiment annotation or labeling is subjective BIBREF0. Annotators often have many disagreements. This is especially s...
{ "question": [ "How does the model differ from Generative Adversarial Networks?", "What is the dataset used to train the model?", "What is the performance of the model?", "Is the model evaluated against a CNN baseline?" ], "question_id": [ "045dbdbda5d96a672e5c69442e30dbf21917a1ee", "c20b...
{ "caption": [ "Figure 1: The proposed NETAB model (left) and its training method (right). Components in light gray color denote that these components are deactivated during training in that stage. (Color online)", "Table 1: Summary statistics of the datasets. Number of positive (P) and negative (N) sentences...
Introduction It is well known that sentiment annotation or labeling is subjective BIBREF0. Annotators often have many disagreements. This is especially so for crowd-workers who are not well trained. That is why one always feels that there are many errors in an annotated dataset. In this paper, we study whether it is po...
1909.00088
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange
In this paper, we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task we call semantic text exchange. This is useful for text data augmentation and the semantic correction of text generated by chatbots and virtual assistants. We introduce a pipeline c...
{ "section_name": [ "Introduction", "Related Work ::: Word and Sentence-level Embeddings", "Related Work ::: Text Infilling", "Related Work ::: Style and Sentiment Transfer", "Related Work ::: Review Generation", "SMERTI ::: Overview", "SMERTI ::: Entity Replacement Module (ERM)", "SME...
{ "question": [ "Does the model proposed beat the baseline models for all the values of the masking parameter tested?", "Has STES been previously used in the literature to evaluate similar tasks?", "What are the baseline models mentioned in the paper?" ], "question_id": [ "dccc3b182861fd19ccce5bd0...
{ "caption": [ "Table 1: Example masked outputs. S is the original input text; RE is the replacement entity; S′′1 corresponds to MRT = 0.2, base ST = 0.4; S ′′ 2 corresponds to MRT = 0.4, base ST = 0.3; S′′3 corresponds to MRT = 0.6, base ST = 0.2; S ′′ 4 corresponds to MRT = 0.8, base ST = 0.1", "Table 2: Tr...
Introduction There has been significant research on style transfer, with the goal of changing the style of text while preserving its semantic content. The alternative where semantics are adjusted while keeping style intact, which we call semantic text exchange (STE), has not been investigated to the best of our knowled...
1911.01799
CN-CELEB: a challenging Chinese speaker recognition dataset
Recently, researchers set an ambitious goal of conducting speaker recognition in unconstrained conditions where the variations on ambient, channel and emotion could be arbitrary. However, most publicly available datasets are collected under constrained environments, i.e., with little noise and limited channel variation...
{ "section_name": [ "Introduction", "The CN-Celeb dataset ::: Data description", "The CN-Celeb dataset ::: Challenges with CN-Celeb", "The CN-Celeb dataset ::: Collection pipeline", "Experiments on speaker recognition", "Experiments on speaker recognition ::: Data", "Experiments on speaker...
{ "question": [ "What was the performance of both approaches on their dataset?", "What kind of settings do the utterances come from?", "What genres are covered?", "Do they experiment with cross-genre setups?", "Which of the two speech recognition models works better overall on CN-Celeb?", "By ...
{ "caption": [ "Table 2. The distribution over utterance length.", "Table 1. The distribution over genres.", "Table 3. Comparison between CN-Celeb and VoxCeleb.", "Table 4. EER(%) results of the i-vector and x-vector systems trained on VoxCeleb and evaluated on three evaluation sets.", "Table 5. E...
Introduction Speaker recognition including identification and verification, aims to recognize claimed identities of speakers. After decades of research, performance of speaker recognition systems has been vastly improved, and the technique has been deployed to a wide range of practical applications. Nevertheless, the p...
1812.06705
Conditional BERT Contextual Augmentation
We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly...
{ "section_name": [ "Introduction", "Fine-tuning on Pre-trained Language Model", "Text Data Augmentation", "Preliminary: Masked Language Model Task", "Conditional BERT", "Conditional BERT Contextual Augmentation", "Experiment", "Datasets", "Text classification", "Connection to ...
{ "question": [ "On what datasets is the new model evaluated on?", "How do the authors measure performance?", "Does the new objective perform better than the original objective bert is trained on?", "Are other pretrained language models also evaluated for contextual augmentation? ", "Do the author...
{ "caption": [ "Figure 1: Model architecture of conditional BERT. The label embeddings in conditional BERT corresponding to segmentation embeddings in BERT, but their functions are different.", "Table 1: Summary statistics for the datasets after tokenization. c: Number of target classes. l: Average sentence l...
Introduction Deep neural network-based models are easy to overfit and result in losing their generalization due to limited size of training data. In order to address the issue, data augmentation methods are often applied to generate more training samples. Recent years have witnessed great success in applying data augme...