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qasper-eval

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1905.08949
Recent Advances in Neural Question Generation
Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. We present a compreh...
{ "section_name": [ "Introduction", "Fundamental Aspects of NQG", "Learning Paradigm", "Input Modality", "Cognitive Levels", "Corpora", "Evaluation Metrics", "Methodology", "Encoding Answers", "Question Word Generation", "Paragraph-level Contexts", "Answer-unaware QG", ...
{ "question": [ "Do they cover data augmentation papers?", "What is the latest paper covered by this survey?", "Do they survey visual question generation work?", "Do they survey multilingual aspects?", "What learning paradigms do they cover in this survey?", "What are all the input modalities ...
{ "caption": [ "Table 1: NQG datasets grouped by their cognitive level and answer type, where the number of documents, the number of questions, and the average number of questions per document (Q./Doc) for each corpus are listed.", "Table 2: Existing NQG models with their best-reported performance on SQuAD. L...
Introduction Question Generation (QG) concerns the task of “automatically generating questions from various inputs such as raw text, database, or semantic representation" BIBREF0 . People have the ability to ask rich, creative, and revealing questions BIBREF1 ; e.g., asking Why did Gollum betray his master Frodo Baggin...
1909.00170
Open Named Entity Modeling from Embedding Distribution
In this paper, we report our discovery on named entity distribution in general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from huaman labo...
{ "section_name": [ "Introduction", "Word Embeddings", "Model", "Open Monolingual NE Modeling", " Embedding Distribution Mapping", "Hypersphere features for NE Recognition ", "Experiment", "Setup", "Monolingual Embedding Distribution", " Hypersphere Mapping", "Off-the-shelf...
{ "question": [ "What is their model?", "Do they evaluate on NER data sets?" ], "question_id": [ "a999761aa976458bbc7b4f330764796446d030ff", "f229069bcb05c2e811e4786c89b0208af90d9a25" ], "nlp_background": [ "infinity", "infinity" ], "topic_background": [ "familiar", "famili...
{ "caption": [ "Table 1: Top-5 Nearest Neighbors.", "Figure 1: Graphical representation of the distribution of the NEs in zh (left) and en (right). Big Xs indicate the center of each entity type, while circles refer to words. Language code: zh-Chinese, en-English, same for all the figures and tables hereafter...
Introduction Named Entity Recognition is a major natural language processing task that recognizes the proper labels such as LOC (Location), PER (Person), ORG (Organization), etc. Like words or phrase, being a sort of language constituent, named entities also benefit from better representation for better processing. Con...
1701.03051
Efficient Twitter Sentiment Classification using Subjective Distant Supervision
As microblogging services like Twitter are becoming more and more influential in today's globalised world, its facets like sentiment analysis are being extensively studied. We are no longer constrained by our own opinion. Others opinions and sentiments play a huge role in shaping our perspective. In this paper, we buil...
{ "section_name": [ "Introduction", "Related Work", "Subjectivity", "Implementation", "Corpus", "Subjectivity Filtering", "Preprocessing", "Baseline model", "Effective Word Score (EFWS) Heuristic", "Training Model", "Evaluation", "Conclusion" ], "paragraphs": [ ...
{ "question": [ "What previously proposed methods is this method compared against?", "How is effective word score calculated?", "How is tweet subjectivity measured?" ], "question_id": [ "6b55b558ed581759425ede5d3a6fcdf44b8082ac", "3e3f5254b729beb657310a5561950085fa690e83", "5bb96b255dab3e4...
{ "caption": [ "Figure 1: Number of tweets with subjectivity greater than the subjectivity threshold", "Figure 3: Comparison of training times for Unigrams", "Figure 2: Variation of accuracy (*Training data of 100K, Test data of 5K) with subjectivity threshold. *TextBlob is used to filter the tweets to fo...
Introduction A lot of work has been done in the field of Twitter sentiment analysis till date. Sentiment analysis has been handled as a Natural Language Processing task at many levels of granularity. Most of these techniques use Machine Learning algorithms with features such as unigrams, n-grams, Part-Of-Speech (POS) t...
1603.01417
Dynamic Memory Networks for Visual and Textual Question Answering
Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong res...
{ "section_name": [ "Introduction", "Dynamic Memory Networks", "Improved Dynamic Memory Networks: DMN+", "Input Module for Text QA", "Input Module for VQA", "The Episodic Memory Module", "Related Work", "Datasets", "bAbI-10k", "DAQUAR-ALL visual dataset", "Visual Question A...
{ "question": [ "Why is supporting fact supervision necessary for DMN?", "What does supporting fact supervision mean?", "What changes they did on input module?", "What improvements they did for DMN?", "How does the model circumvent the lack of supporting facts during training?", "Does the DMN+...
{ "caption": [ "Figure 1. Question Answering over text and images using a Dynamic Memory Network.", "Figure 2. The input module with a “fusion layer”, where the sentence reader encodes the sentence and the bi-directional GRU allows information to flow between sentences.", "Figure 3. VQA input module to re...
Introduction Neural network based methods have made tremendous progress in image and text classification BIBREF0 , BIBREF1 . However, only recently has progress been made on more complex tasks that require logical reasoning. This success is based in part on the addition of memory and attention components to complex neu...
1911.03385
Low-Level Linguistic Controls for Style Transfer and Content Preservation
Despite the success of style transfer in image processing, it has seen limited progress in natural language generation. Part of the problem is that content is not as easily decoupled from style in the text domain. Curiously, in the field of stylometry, content does not figure prominently in practical methods of discrim...
{ "section_name": [ "Introduction", "Related Work ::: Style Transfer with Parallel Data", "Related Work ::: Style Transfer without Parallel Data", "Related Work ::: Controlling Linguistic Features", "Related Work ::: Stylometry and the Digital Humanities", "Models ::: Preliminary Classificatio...
{ "question": [ "Is this style generator compared to some baseline?", "How they perform manual evaluation, what is criteria?", "What metrics are used for automatic evaluation?", "How they know what are content words?", "How they model style as a suite of low-level linguistic controls, such as freq...
{ "caption": [ "Table 1: The size of the data across the three different styles investigated.", "Table 2: Accuracy of five classifiers trained using trigrams with fasttext, for all test data and split by genre. Despite heavy ablation, the Ablated NVA classifier has an accuracy of 75%, suggesting synactic and ...
Introduction All text has style, whether it be formal or informal, polite or aggressive, colloquial, persuasive, or even robotic. Despite the success of style transfer in image processing BIBREF0, BIBREF1, there has been limited progress in the text domain, where disentangling style from content is particularly difficu...
1902.06843
Fusing Visual, Textual and Connectivity Clues for Studying Mental Health
With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media. Unlike the most existing efforts which study depression by analyzing te...
{ "section_name": [ null, "Introduction", "Related Work", "Dataset", "Data Modality Analysis", "Demographic Prediction", "Multi-modal Prediction Framework" ], "paragraphs": [ [ "0pt*0*0", "0pt*0*0", "0pt*0*0 0.95", "1]Amir Hossein Yazdavar 1]Mohammad Saeid M...
{ "question": [ "Do they report results only on English data?", "What insights into the relationship between demographics and mental health are provided?", "What model is used to achieve 5% improvement on F1 for identifying depressed individuals on Twitter?", "How do this framework facilitate demograp...
{ "caption": [ "Figure 1: Self-disclosure on Twitter from likely depressed users discovered by matching depressiveindicative terms", "Figure 2: The age distribution for depressed and control users in ground-truth dataset", "Figure 3: Gender and Depressive Behavior Association (Chi-square test: color-code:...
None 0pt*0*0 0pt*0*0 0pt*0*0 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright State University, OH, USA [2]Ohio State University...
1905.06512
Incorporating Sememes into Chinese Definition Modeling
Chinese definition modeling is a challenging task that generates a dictionary definition in Chinese for a given Chinese word. To accomplish this task, we construct the Chinese Definition Modeling Corpus (CDM), which contains triples of word, sememes and the corresponding definition. We present two novel models to impro...
{ "section_name": [ "Introduction", "Methodology", "Baseline Model", "Adaptive-Attention Model", "Self- and Adaptive-Attention Model", "Experiments", "Dataset", "Settings", "Results", "Definition Modeling", "Knowledge Bases", "Self-Attention", "Conclusion" ], "p...
{ "question": [ "Is there an online demo of their system?", "Do they perform manual evaluation?", "Do they compare against Noraset et al. 2017?", "What is a sememe?" ], "question_id": [ "e21a8581cc858483a31c6133e53dd0cfda76ae4c", "9f6e877e3bde771595e8aee10c2656a0e7b9aeb2", "a3783e42c2b...
{ "caption": [ "Figure 1: An example of the CDM dataset. The word “旅馆” (hotel) has five sememes, which are “场所” (place), “旅游” (tour), “吃” (eat), “娱乐” (recreation) and “住下” (reside).", "Figure 2: An overview of the decoder for the SAAM. The left sub-figure shows our decoder contains N identical layers, where e...
Introduction Chinese definition modeling is the task of generating a definition in Chinese for a given Chinese word. This task can benefit the compilation of dictionaries, especially dictionaries for Chinese as a foreign language (CFL) learners. In recent years, the number of CFL learners has risen sharply. In 2017, 7...
2001.06286
RobBERT: a Dutch RoBERTa-based Language Model
Pre-trained language models have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural language tasks. One of the most prominent pre-trained language models is BERT (Bi-directional Encoders for Transformers), which was release...
{ "section_name": [ "Introduction", "Related Work", "Pre-training RobBERT", "Pre-training RobBERT ::: Data", "Pre-training RobBERT ::: Training", "Evaluation", "Evaluation ::: Sentiment Analysis", "Evaluation ::: Die/Dat Disambiguation", "Code", "Future Work", "Conclusion",...
{ "question": [ "What data did they use?", "What is the state of the art?", "What language tasks did they experiment on?" ], "question_id": [ "589be705a5cc73a23f30decba23ce58ec39d313b", "6e962f1f23061f738f651177346b38fd440ff480", "594a6bf37eab64a16c6a05c365acc100e38fcff1" ], "nlp_backg...
{ "caption": [ "Table 1: Results of RobBERT fine-tuned on several downstream tasks compared to the state of the art on the tasks. For accuracy, we also report the 95% confidence intervals. (Results annotated with * from van der Burgh and Verberne (2019), ** = from de Vries et al. (2019), *** from Allein et al. (2...
Introduction The advent of neural networks in natural language processing (NLP) has significantly improved state-of-the-art results within the field. While recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) initially dominated the field, recent models started incorporating attention mechanisms...
1910.02789
Natural Language State Representation for Reinforcement Learning
Recent advances in Reinforcement Learning have highlighted the difficulties in learning within complex high dimensional domains. We argue that one of the main reasons that current approaches do not perform well, is that the information is represented sub-optimally. A natural way to describe what we observe, is through ...
{ "section_name": [ "Introduction", "Preliminaries ::: Reinforcement Learning", "Preliminaries ::: Deep Learning for NLP", "Semantic Representation Methods", "Semantic State Representations in the Doom Environment", "Semantic State Representations in the Doom Environment ::: Experiments", ...
{ "question": [ "What result from experiments suggest that natural language based agents are more robust?", "How better is performance of natural language based agents in experiments?", "How much faster natural language agents converge in performed experiments?", "What experiments authors perform?", ...
{ "caption": [ "Figure 1: Example of Semantic Segmentation [Kundu et al., 2016].", "Figure 2: Left: Raw visual inputs and their corresponding semantic segmentation in the VizDoom enviornment. Right: Our suggested NLP-based semantic state representation framework.", "Figure 3: Frame division used for descr...
Introduction “The world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations." (Edward Sapir, Language: An Introduction to the Study of Speech, 1921) Deep Learning based algorithms use neural networks in orde...
1902.00672
Query-oriented text summarization based on hypergraph transversals
Existing graph- and hypergraph-based algorithms for document summarization represent the sentences of a corpus as the nodes of a graph or a hypergraph in which the edges represent relationships of lexical similarities between sentences. Each sentence of the corpus is then scored individually, using popular node ranking...
{ "section_name": [ "Introduction", "Background and related work", "Problem statement and system overview", "Summarization based on hypergraph transversals", "Preprocessing and similarity computation", "Sentence theme detection based on topic tagging", "Sentence hypergraph construction", ...
{ "question": [ "How does the model compare with the MMR baseline?" ], "question_id": [ "babe72f0491e65beff0e5889380e8e32d7a81f78" ], "nlp_background": [ "infinity" ], "topic_background": [ "familiar" ], "paper_read": [ "no" ], "search_query": [ "summarization" ], "ques...
{ "caption": [ "Figure 1: Algorithm Chart.", "Figure 2: Example of hypergraph and minimal hypergraph transversal.", "Figure 3: ROUGE-2 and ROUGE-SU4 as a function of δ for λ = 0.4 and µ = 1.98.", "Figure 4: ROUGE-2 and ROUGE-SU4 as a function of λ for δ = 0.85 and µ = 1.98.", "Figure 5: Evolution ...
Introduction The development of automatic tools for the summarization of large corpora of documents has attracted a widespread interest in recent years. With fields of application ranging from medical sciences to finance and legal science, these summarization systems considerably reduce the time required for knowledge ...
2001.07209
Text-based inference of moral sentiment change
We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora. Our framework is based on the premise that language use can inform people's moral perception toward right or wrong, and we build our methodology by exploring moral biases learned from diachronic word embed...
{ "section_name": [ "Moral sentiment change and language", "Emerging NLP research on morality", "A three-tier modelling framework", "A three-tier modelling framework ::: Lexical data for moral sentiment", "A three-tier modelling framework ::: Models", "Historical corpus data", "Model evalu...
{ "question": [ "Does the paper discuss previous models which have been applied to the same task?", "Which datasets are used in the paper?", "How does the parameter-free model work?", "How do they quantify moral relevance?", "Which fine-grained moral dimension examples do they showcase?", "Whi...
{ "caption": [ "Figure 1: Illustration of moral sentiment change over the past two centuries. Moral sentiment trajectories of three probe concepts, slavery, democracy, and gay, are shown in moral sentiment embedding space through 2D projection from Fisher’s discriminant analysis with respect to seed words from th...
Moral sentiment change and language People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral views evolve has been a long-standing issue and a constant source of contr...
2001.10161
Bringing Stories Alive: Generating Interactive Fiction Worlds
World building forms the foundation of any task that requires narrative intelligence. In this work, we focus on procedurally generating interactive fiction worlds---text-based worlds that players "see" and "talk to" using natural language. Generating these worlds requires referencing everyday and thematic commonsense p...
{ "section_name": [ "Introduction", "Related Work", "World Generation", "World Generation ::: Knowledge Graph Construction", "World Generation ::: Knowledge Graph Construction ::: Neural Graph Construction", "World Generation ::: Knowledge Graph Construction ::: Rule-Based Graph Construction",...
{ "question": [ "How well did the system do?", "How is the information extracted?" ], "question_id": [ "c180f44667505ec03214d44f4970c0db487a8bae", "76d62e414a345fe955dc2d99562ef5772130bc7e" ], "nlp_background": [ "two", "two" ], "topic_background": [ "unfamiliar", "unfamili...
{ "caption": [ "Figure 1: Example player interaction in the deep neural generated mystery setting.", "Figure 2: Example knowledge graph constructed by AskBERT.", "Figure 3: Overall AskBERT pipeline for graph construction.", "Figure 4: Overview for neural description generation.", "Table 3: Results...
Introduction Interactive fictions—also called text-adventure games or text-based games—are games in which a player interacts with a virtual world purely through textual natural language—receiving descriptions of what they “see” and writing out how they want to act, an example can be seen in Figure FIGREF2. Interactive ...
1909.00279
Generating Classical Chinese Poems from Vernacular Chinese
Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model. In this paper, we propose a novel task of generating classical Chinese poems fro...
{ "section_name": [ "Introduction", "Related Works", "Model ::: Main Architecture", "Model ::: Addressing Under-Translation and Over-Translation", "Model ::: Addressing Under-Translation and Over-Translation ::: Under-Translation", "Model ::: Addressing Under-Translation and Over-Translation :...
{ "question": [ "What are some guidelines in writing input vernacular so model can generate ", "How much is proposed model better in perplexity and BLEU score than typical UMT models?", "What dataset is used for training?" ], "question_id": [ "6b9310b577c6232e3614a1612cbbbb17067b3886", "d484a7...
{ "caption": [ "Figure 1: An example of the training procedures of our model. Here we depict two procedures, namely back translation and language modeling. Back translation has two paths, namely ES → DT → ET → DS and DT → ES → DS → ET . Language modeling also has two paths, namely ET → DT and ES → DS . Figure 1 s...
Introduction During thousands of years, millions of classical Chinese poems have been written. They contain ancient poets' emotions such as their appreciation for nature, desiring for freedom and concerns for their countries. Among various types of classical poetry, quatrain poems stand out. On the one hand, their aest...
1909.06762
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue generation work treats the KB query as an attention over the entire KB, without the guarantee that the generated entities are consistent with each other. In this p...
{ "section_name": [ "Introduction", "Definition", "Definition ::: Dialogue History", "Definition ::: Knowledge Base", "Definition ::: Seq2Seq Dialogue Generation", "Our Framework", "Our Framework ::: Encoder", "Our Framework ::: Vanilla Attention-based Decoder", "Our Framework ::: ...
{ "question": [ "What were the evaluation metrics?", "What were the baseline systems?", "Which dialog datasets did they experiment with?", "What KB is used?" ], "question_id": [ "ee31c8a94e07b3207ca28caef3fbaf9a38d94964", "66d743b735ba75589486e6af073e955b6bb9d2a4", "b9f852256113ef468d6...
{ "caption": [ "Figure 1: An example of a task-oriented dialogue that incorporates a knowledge base (KB). The fourth row in KB supports the second turn of the dialogue. A dialogue system will produce a response with conflict entities if it includes the POI in the fourth row and the address in the fifth row, like ...
Introduction Task-oriented dialogue system, which helps users to achieve specific goals with natural language, is attracting more and more research attention. With the success of the sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, several works tried to model the ta...
1812.07023
From FiLM to Video: Multi-turn Question Answering with Multi-modal Context
Understanding audio-visual content and the ability to have an informative conversation about it have both been challenging areas for intelligent systems. The Audio Visual Scene-aware Dialog (AVSD) challenge, organized as a track of the Dialog System Technology Challenge 7 (DSTC7), proposes a combined task, where a syst...
{ "section_name": [ "Introduction", "Related Work", "The avsd dataset and challenge", "Models", "Utterance-level Encoder", "Description Encoder", "Video Encoder with Time-Extended FiLM", "Audio Encoder", "Fusing Modalities for Dialogue Context", "Decoders", "Loss Function",...
{ "question": [ "At which interval do they extract video and audio frames?", "Do they use pretrained word vectors for dialogue context embedding?", "Do they train a different training method except from scheduled sampling?" ], "question_id": [ "05e3b831e4c02bbd64a6e35f6c52f0922a41539a", "bd744...
{ "caption": [ "Table 1: Tasks with audio, visual and text modalities", "Figure 1: FA-HRED uses the last question’s encoding to attend to video description, audio, and video features. These features along with the dialogue state enable the model to generate the answer to the current question. The ground truth...
Introduction Deep neural networks have been successfully applied to several computer vision tasks such as image classification BIBREF0 , object detection BIBREF1 , video action classification BIBREF2 , etc. They have also been successfully applied to natural language processing tasks such as machine translation BIBREF3...
1610.04377
Civique: Using Social Media to Detect Urban Emergencies
We present the Civique system for emergency detection in urban areas by monitoring micro blogs like Tweets. The system detects emergency related events, and classifies them into appropriate categories like"fire","accident","earthquake", etc. We demonstrate our ideas by classifying Twitter posts in real time, visualizin...
{ "section_name": [ "Introduction", "Motivation and Challenges", "Our Approach", "Pre-Processing Modules", "Emergency Classification", "Type Classification", "Location Visualizer", "Evaluation", "Dataset Creation", "Classifier Evaluation", "Demostration Description", "C...
{ "question": [ "Is the web interface publicly accessible?", "Is the Android application publicly available?", "What classifier is used for emergency categorization?", "What classifier is used for emergency detection?", "Do the tweets come from any individual?", "How many categories are there?...
{ "caption": [ "Fig. 1. System Architecture", "Table 2. Sample output of Compression module", "Table 4. Classification Results", "Fig. 2. Word Cloud of top attributes", "Fig. 3. Screenshot: Web Interface", "Fig. 4. Screenshot: Mobile Interface Fig. 5. Screenshot: Generated Notification" ], ...
Introduction With the surge in the use of social media, micro-blogging sites like Twitter, Facebook, and Foursquare have become household words. Growing ubiquity of mobile phones in highly populated developing nations has spurred an exponential rise in social media usage. The heavy volume of social media posts tagged w...
1906.06448
Can neural networks understand monotonicity reasoning?
Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures. Since no test set has been developed for monotonicity reasoning with wide coverage, it is still ...
{ "section_name": [ "Introduction", "Monotonicity", "Human-oriented dataset", "Linguistics-oriented dataset", "Statistics", "Baselines", "Data augmentation for analysis", "Discussion", "Conclusion", "Acknowledgement" ], "paragraphs": [ [ "Natural language inferenc...
{ "question": [ "Do they release MED?", "What NLI models do they analyze?", "How do they define upward and downward reasoning?", "What is monotonicity reasoning?" ], "question_id": [ "c0a11ba0f6bbb4c69b5a0d4ae9d18e86a4a8f354", "dfc393ba10ec4af5a17e5957fcbafdffdb1a6443", "311a7fa62721e8...
{ "caption": [ "Table 1: Determiners and their polarities.", "Table 2: Examples of downward operators.", "Figure 1: Overview of our human-oriented dataset creation. E: entailment, NE: non-entailment.", "Table 3: Numbers of cases where answers matched automatically determined gold labels.", "Table ...
Introduction Natural language inference (NLI), also known as recognizing textual entailment (RTE), has been proposed as a benchmark task for natural language understanding. Given a premise $P$ and a hypothesis $H$ , the task is to determine whether the premise semantically entails the hypothesis BIBREF0 . A number of r...
1912.00819
Enriching Existing Conversational Emotion Datasets with Dialogue Acts using Neural Annotators.
The recognition of emotion and dialogue acts enrich conversational analysis and help to build natural dialogue systems. Emotion makes us understand feelings and dialogue acts reflect the intentions and performative functions in the utterances. However, most of the textual and multi-modal conversational emotion datasets...
{ "section_name": [ "Introduction", "Annotation of Emotional Dialogue Acts ::: Data for Conversational Emotion Analysis", "Annotation of Emotional Dialogue Acts ::: Dialogue Act Tagset and SwDA Corpus", "Annotation of Emotional Dialogue Acts ::: Neural Model Annotators", "Annotation of Emotional D...
{ "question": [ "What other relations were found in the datasets?", "How does the ensemble annotator extract the final label?", "How were dialogue act labels defined?", "How many models were used?" ], "question_id": [ "5937ebbf04f62d41b48cbc6b5c38fc309e5c2328", "dcd6f18922ac5c00c22cef33c53...
{ "caption": [ "Figure 1: Emotional Dialogue Acts: Example of a dialogue from MELD representing emotions and sentiment (rectangular boxes), in our work, we add dialogue acts (rounded boxes). Image source Poria et al. (2019).", "Figure 2: Setting of the annotation process of the EDAs, above example utterances ...
Introduction With the growing demand for human-computer/robot interaction systems, detecting the emotional state of the user can heavily benefit a conversational agent to respond at an appropriate emotional level. Emotion recognition in conversations has proven important for potential applications such as response reco...
1907.00758
Synchronising audio and ultrasound by learning cross-modal embeddings
Audiovisual synchronisation is the task of determining the time offset between speech audio and a video recording of the articulators. In child speech therapy, audio and ultrasound videos of the tongue are captured using instruments which rely on hardware to synchronise the two modalities at recording time. Hardware sy...
{ "section_name": [ "Introduction", "Background", "Audiovisual synchronisation for lip videos", "Lip videos vs. ultrasound tongue imaging (UTI)", "Model", "Data", "Preparing the data", "Creating samples using a self-supervision strategy", "Dividing samples for training, validation ...
{ "question": [ "Do they compare their neural network against any other model?", "Do they annotate their own dataset or use an existing one?", "Does their neural network predict a single offset in a recording?", "What kind of neural network architecture do they use?" ], "question_id": [ "73d65...
{ "caption": [ "Figure 1: UltraSync maps high dimensional inputs to low dimensional vectors using a contrastive loss function, such that the Euclidean distance is small between vectors from positive pairs and large otherwise. Inputs span '200ms: 5 consecutive raw ultrasound frames on one stream and 20 frames of t...
Introduction Ultrasound tongue imaging (UTI) is a non-invasive way of observing the vocal tract during speech production BIBREF0 . Instrumental speech therapy relies on capturing ultrasound videos of the patient's tongue simultaneously with their speech audio in order to provide a diagnosis, design treatments, and meas...
1710.06536
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, ...
{ "section_name": [ "Affiliation", "Synonyms", "Glossary", "Definition", "Key Points", "Historical Background", "Introduction", "Subjectivity detection", "Aspect-Based Sentiment Analysis", "Preliminaries", "Gaussian Bayesian Networks", "Convolutional Neural Networks", ...
{ "question": [ "How are aspects identified in aspect extraction?" ], "question_id": [ "3bf429633ecbbfec3d7ffbcfa61fa90440cc918b" ], "nlp_background": [ "infinity" ], "topic_background": [ "familiar" ], "paper_read": [ "no" ], "search_query": [ "" ], "question_writer": ...
{ "caption": [ "Fig. 1 State space of different Bayesian models", "Fig. 2 State space of Bayesian CNN where the input layer is pre-trained using a dynamic GBN", "Table 2 SemEval Data used for Evaluation", "Fig. 3 Comparison of the performance with the state of the art.", "Table 3 Random features v...
Affiliation School of Computer Science and Engineering, Nanyang Technological University, Singapore Synonyms Sentiment Analysis, Subjectivity Detection, Deep Learning Aspect Extraction, Polarity Distribution, Convolutional Neural Network. Glossary Aspect : Feature related to an opinion target Convolution : features ...
1701.02877
Generalisation in Named Entity Recognition: A Quantitative Analysis
Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, featur...
{ "section_name": [ "Introduction", "Datasets", "NER Models and Features", "RQ1: NER performance with Different Approaches", "RQ2: NER performance in Different Genres", "RQ3: Impact of NE Diversity", "RQ4: Unseen Features, unseen NEs and NER performance", "RQ5: Out-Of-Domain NER Perfor...
{ "question": [ "What web and user-generated NER datasets are used for the analysis?" ], "question_id": [ "94e0cf44345800ef46a8c7d52902f074a1139e1a" ], "nlp_background": [ "five" ], "topic_background": [ "familiar" ], "paper_read": [ "somewhat" ], "search_query": [ "named e...
{ "caption": [ "Table 1 Corpora genres and number of NEs of different classes.", "Table 2 Sizes of corpora, measured in number of NEs, used for training and testing. Note that the for the ConLL corpus the dev set is called “Test A” and the test set “Test B”.", "Table 3 P, R and F1 of NERC with different m...
Introduction Named entity recognition and classification (NERC, short NER), the task of recognising and assigning a class to mentions of proper names (named entities, NEs) in text, has attracted many years of research BIBREF0 , BIBREF1 , analyses BIBREF2 , starting from the first MUC challenge in 1995 BIBREF3 . Recogni...
1904.05862
wav2vec: Unsupervised Pre-training for Speech Recognition
We explore unsupervised pre-training for speech recognition by learning representations of raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting representations are then used to improve acoustic model training. We pre-train a simple multi-layer convolutional neural network optimized v...
{ "section_name": [ "Introduction", "Pre-training Approach", "Model", "Objective", "Data", "Acoustic Models", "Decoding", "Pre-training Models", "Results", "Pre-training for the WSJ benchmark", "Pre-training for TIMIT", "Ablations", "Conclusions", "Acknowledgeme...
{ "question": [ "Which unlabeled data do they pretrain with?", "How many convolutional layers does their model have?", "Do they explore how much traning data is needed for which magnitude of improvement for WER? " ], "question_id": [ "ad67ca844c63bf8ac9fdd0fa5f58c5a438f16211", "12eaaf3b6ebc518...
{ "caption": [ "Figure 1: Illustration of pre-training from audio data X which is encoded with two convolutional neural networks that are stacked on top of each other. The model is optimized to solve a next time step prediction task.", "Table 1: Replacing log-mel filterbanks (Baseline) by pre-trained embeddin...
Introduction Current state of the art models for speech recognition require large amounts of transcribed audio data to attain good performance BIBREF1 . Recently, pre-training of neural networks has emerged as an effective technique for settings where labeled data is scarce. The key idea is to learn general representat...
1708.09157
Cross-lingual, Character-Level Neural Morphological Tagging
Even for common NLP tasks, sufficient supervision is not available in many languages -- morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and lo...
{ "section_name": [ "Introduction", "Morphological Tagging", "Character-Level Neural Transfer", "Character-Level Neural Networks", "Cross-Lingual Morphological Transfer as Multi-Task Learning", "Experiments", "Experimental Languages", "Datasets", "Baselines", "Experimental Deta...
{ "question": [ "How are character representations from various languages joint?", "On which dataset is the experiment conducted?" ], "question_id": [ "a43c400ae37a8705ff2effb4828f4b0b177a74c4", "4056ee2fd7a0a0f444275e627bb881134a1c2a10" ], "nlp_background": [ "two", "two" ], "topi...
{ "caption": [ "Figure 1: Example of a morphologically tagged sentence in Russian using the annotation scheme provided in the UD dataset.", "Table 1: Partial inflection table for the Spanish verb soñar", "Figure 2: We depict four subarchitectures used in the models we develop in this work. Combining (a) ...
Introduction State-of-the-art morphological taggers require thousands of annotated sentences to train. For the majority of the world's languages, however, sufficient, large-scale annotation is not available and obtaining it would often be infeasible. Accordingly, an important road forward in low-resource NLP is the dev...
1911.00069
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challengi...
{ "section_name": [ "Introduction", "Overview of the Approach", "Cross-Lingual Word Embeddings", "Cross-Lingual Word Embeddings ::: Monolingual Word Embeddings", "Cross-Lingual Word Embeddings ::: Bilingual Word Embedding Mapping", "Cross-Lingual Word Embeddings ::: Bilingual Word Embedding Ma...
{ "question": [ "Do they train their own RE model?", "How big are the datasets?", "What languages do they experiment on?", "What datasets are used?" ], "question_id": [ "f6496b8d09911cdf3a9b72aec0b0be6232a6dba1", "5c90e1ed208911dbcae7e760a553e912f8c237a5", "3c3b4797e2b21e2c31cf117ad9e5...
{ "caption": [ "Figure 1: Neural cross-lingual relation extraction based on bilingual word embedding mapping - target language: Portuguese, source language: English.", "Table 1: Comparison with the state-of-the-art RE models on the ACE05 English data (S: Single Model; E: Ensemble Model).", "Table 2: Numbe...
Introduction Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities like persons, organizations, geo-political entities, locations, and events. It provides useful information for many NLP applications such as knowledge base construc...
1910.04887
Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities
We present a new task of query auto-completion for estimating instance probabilities. We complete a user query prefix conditioned upon an image. Given the complete query, we fine tune a BERT embedding for estimating probabilities of a broad set of instances. The resulting instance probabilities are used for selection w...
{ "section_name": [ "Introduction", "Methods", "Methods ::: Modifying FactorCell LSTM for Image Query Auto-Completion", "Methods ::: Fine Tuning BERT for Instance Probability Estimation", "Methods ::: Data and Training Details", "Results", "Results ::: Conclusions" ], "paragraphs": [ ...
{ "question": [ "How better does auto-completion perform when using both language and vision than only language?", "How big is data provided by this research?", "How they complete a user query prefix conditioned upon an image?" ], "question_id": [ "dfb0351e8fa62ceb51ce77b0f607885523d1b8e8", "a...
{ "caption": [ "Figure 1: Architecture: Image features are extracted from a pretrained CNN along with the user query prefix are input to an extended FactorCell LSTM which outputs a completed query. The completed query is fed into a fine-tuned BERT embedding which outputs instance probabilities used for instance s...
Introduction This work focuses on the problem of finding objects in an image based on natural language descriptions. Existing solutions take into account both the image and the query BIBREF0, BIBREF1, BIBREF2. In our problem formulation, rather than having the entire text, we are given only a prefix of the text which r...
1810.00663
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model's performan...
{ "section_name": [ "Introduction", "Related work", "Problem Formulation", "The Behavioral Graph: A Knowledge Base For Navigation", "Approach", "Dataset", "Experiments", "Evaluation Metrics", "Models Used in the Evaluation", "Implementation Details", "Quantitative Evaluatio...
{ "question": [ "Did the collection process use a WoZ method?", "By how much did their model outperform the baseline?", "What baselines did they compare their model with?", "What was the performance of their model?", "What evaluation metrics are used?", "Did the authors use a crowdsourcing pla...
{ "caption": [ "Figure 1: Map of an environment (a), its (partial) behavioral navigation graph (b), and the problem setting of interest (c). The red part of (b) corresponds to the representation of the route highlighted in blue in (a). The codes “oo-left”, “oo-right”, “cf”, “left-io”, and “right-io” correspond to...
Introduction Enabling robots to follow navigation instructions in natural language can facilitate human-robot interaction across a variety of applications. For instance, within the service robotics domain, robots can follow navigation instructions to help with mobile manipulation BIBREF0 and delivery tasks BIBREF1 . I...
1809.05752
Analysis of Risk Factor Domains in Psychosis Patient Health Records
Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health recor...
{ "section_name": [ "Introduction", "Related Work", "Data", "Annotation Task", "Inter-Annotator Agreement", "Topic Extraction", "Results and Discussion", "Future Work and Conclusion", "Acknowledgments" ], "paragraphs": [ [ "Psychotic disorders typically emerge in late...
{ "question": [ "What additional features are proposed for future work?", "What are their initial results on this task?", "What datasets did the authors use?" ], "question_id": [ "c82e945b43b2e61c8ea567727e239662309e9508", "fbee81a9d90ff23603ee4f5986f9e8c0eb035b52", "39cf0b3974e8a19f3745ad...
{ "caption": [ "Table 1: Demographic breakdown of the target cohort.", "Table 2: Annotation scheme for the domain classification task.", "Table 3: Inter-annotator agreement", "Table 4: Architectures of our highest-performing MLP and RBF networks.", "Figure 1: Data pipeline for training and evaluat...
Introduction Psychotic disorders typically emerge in late adolescence or early adulthood BIBREF0 , BIBREF1 and affect approximately 2.5-4% of the population BIBREF2 , BIBREF3 , making them one of the leading causes of disability worldwide BIBREF4 . A substantial proportion of psychiatric inpatients are readmitted after...
2001.01589
Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation
Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years. However, in consideration of efficiency, a limited-size vocabulary that only contains the top-N highest frequency words are employed for model training, which leads to many rare and unknown words. It is rat...
{ "section_name": [ "Introduction", "Approach", "Approach ::: Morpheme Segmentation", "Approach ::: Morpheme Segmentation ::: Stem with Combined Suffix", "Approach ::: Morpheme Segmentation ::: Stem with Singular Suffix", "Approach ::: Byte Pair Encoding (BPE)", "Approach ::: Morphological...
{ "question": [ "How many linguistic and semantic features are learned?", "How is morphology knowledge implemented in the method?", "How does the word segmentation method work?", "Is the word segmentation method independently evaluated?" ], "question_id": [ "1f6180bba0bc657c773bd3e4269f87540a5...
{ "caption": [ "Table 1: The sentence examples with different segmentation strategies for Turkish-English.", "Table 2: The training corpus statistics of TurkishEnglish machine translation task.", "Table 3: The training corpus statistics of UyghurChinese machine translation task.", "Table 4: The traini...
Introduction Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years for many language pairs BIBREF0, BIBREF1, BIBREF2. However, in consideration of time cost and space capacity, the NMT model generally employs a limited-size vocabulary that only contains the top...
1910.10324
Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks
Deep acoustic models typically receive features in the first layer of the network, and process increasingly abstract representations in the subsequent layers. Here, we propose to feed the input features at multiple depths in the acoustic model. As our motivation is to allow acoustic models to re-examine their input fea...
{ "section_name": [ "Introduction", "Transformer Modules", "Iterated Feature Presentation", "Iterated Feature Presentation ::: Feature Re-Presentation", "Iterated Feature Presentation ::: Iterated Loss", "Experimental results ::: Dataset", "Experimental results ::: Target Units", "Expe...
{ "question": [ "Do they normalize the calculated intermediate output hypotheses to compensate for the incompleteness?", "How many layers do they use in their best performing network?", "Do they just sum up all the loses the calculate to end up with one single loss?", "Does their model take more time ...
{ "caption": [ "Fig. 2. A 24 layer transformer with one auxiliary loss and feature re-presentation in the 12-th layer. Z0 represents the input features. Orange boxes represent an additional MLP network and softmax. Green boxes represent linear projections and layer-norm.", "Fig. 3. Merging input features and ...
Introduction In this paper, we propose the processing of features not only in the input layer of a deep network, but in the intermediate layers as well. We are motivated by a desire to enable a neural network acoustic model to adaptively process the features depending on partial hypotheses and noise conditions. Many pr...
1910.05456
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
How does knowledge of one language's morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, we perform experiments with a sequence-to-sequence architecture, which we train on different combinations of eight source and three target la...
{ "section_name": [ "Introduction", "Task", "Task ::: Formal definition.", "Model ::: Pointer–Generator Network", "Model ::: Pointer–Generator Network ::: Encoders.", "Model ::: Pointer–Generator Network ::: Attention.", "Model ::: Pointer–Generator Network ::: Decoder.", "Model ::: Pr...
{ "question": [ "Are agglutinative languages used in the prediction of both prefixing and suffixing languages?", "What is an example of a prefixing language?", "How is the performance on the task evaluated?", "What are the tree target languages studied in the paper?" ], "question_id": [ "fc29b...
{ "caption": [ "Table 1: Paradigms of the English lemmas dance and eat. dance has 4 distinct inflected forms; eat has 5.", "Table 2: WALS features from the Morphology category. 20A: 0=Exclusively concatenative, 1=N/A. 21A: 0=No case, 1=Monoexponential case, 2=Case+number, 3=N/A. 21B: 0=monoexponential TAM, 1=...
Introduction A widely agreed-on fact in language acquisition research is that learning of a second language (L2) is influenced by a learner's native language (L1) BIBREF0, BIBREF1. A language's morphosyntax seems to be no exception to this rule BIBREF2, but the exact nature of this influence remains unknown. For instan...
1910.05154
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
For language documentation initiatives, transcription is an expensive resource: one minute of audio is estimated to take one hour and a half on average of a linguist's work (Austin and Sallabank, 2013). Recently, collecting aligned translations in well-resourced languages became a popular solution for ensuring posterio...
{ "section_name": [ "Introduction", "Methodology ::: The Multilingual Mboshi Parallel Corpus:", "Methodology ::: Bilingual Unsupervised Word Segmentation/Discovery Approach:", "Methodology ::: Multilingual Leveraging:", "Experiments", "Conclusion" ], "paragraphs": [ [ "The Cambri...
{ "question": [ "Is the model evaluated against any baseline?", "Does the paper report the accuracy of the model?", "How is the performance of the model evaluated?", "What are the different bilingual models employed?", "How does the well-resourced language impact the quality of the output?" ], ...
{ "caption": [ "Table 2: From left to right, results for: bilingual UWS, multilingual leveraging by voting, ANE selection.", "Table 1: Statistics for the Multilingual Mboshi parallel corpus. The French text is used for generating translation in the four other languages present in the right side of the table."...
Introduction The Cambridge Handbook of Endangered Languages BIBREF3 estimates that at least half of the 7,000 languages currently spoken worldwide will no longer exist by the end of this century. For these endangered languages, data collection campaigns have to accommodate the challenge that many of them are from oral ...
1806.00722
Dense Information Flow for Neural Machine Translation
Recently, neural machine translation has achieved remarkable progress by introducing well-designed deep neural networks into its encoder-decoder framework. From the optimization perspective, residual connections are adopted to improve learning performance for both encoder and decoder in most of these deep architectures...
{ "section_name": [ "Introduction", "DenseNMT", "Dense encoder and decoder", "Dense attention", "Summary layers", "Analysis of information flow", "Datasets", "Model and architect design", "Training setting", "Training curve", "DenseNMT improves accuracy with similar archite...
{ "question": [ "what are the baselines?", "did they outperform previous methods?", "what language pairs are explored?", "what datasets were used?" ], "question_id": [ "26b5c090f72f6d51e5d9af2e470d06b2d7fc4a98", "8c0621016e96d86a7063cb0c9ec20c76a2dba678", "f1214a05cc0e6d870c789aed24a8d...
{ "caption": [ "Figure 1: Comparison of dense-connected encoder and residual-connected encoder. Left: regular residual-connected encoder. Right: dense-connected encoder. Information is directly passed from blue blocks to the green block.", "Figure 2: Comparison of dense-connected decoder and residual-connecte...
Introduction Neural machine translation (NMT) is a challenging task that attracts lots of attention in recent years. Starting from the encoder-decoder framework BIBREF0 , NMT starts to show promising results in many language pairs. The evolving structures of NMT models in recent years have made them achieve higher scor...
2003.03612
Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text
There is inherent information captured in the order in which we write words in a list. The orderings of binomials --- lists of two words separated by `and' or `or' --- has been studied for more than a century. These binomials are common across many areas of speech, in both formal and informal text. In the last century,...
{ "section_name": [ "Introduction", "Introduction ::: Related Work", "Data", "Dimensions of Binomials", "Dimensions of Binomials ::: Definitions", "Dimensions of Binomials ::: Dimensions", "Models And Predictions", "Models And Predictions ::: Stability of Asymmetry", "Models And Pr...
{ "question": [ "How is order of binomials tracked across time?", "What types of various community texts have been investigated for exploring global structure of binomials?", "Are there any new finding in analasys of trinomials that was not present binomials?", "What new model is proposed for binomial...
{ "caption": [ "Figure 1: Histogram of comment timestamps for r/nba and r/nfl. Both subreddits exhibit a seasonal structure. The number of comments is increasing for all subreddits.", "Table 1: Summary statistics of subreddit list data that we investigate in this paper.", "Figure 2: A histogram of the log...
Introduction Lists are extremely common in text and speech, and the ordering of items in a list can often reveal information. For instance, orderings can denote relative importance, such as on a to-do list, or signal status, as is the case for author lists of scholarly publications. In other cases, orderings might come...
1904.08386
Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism
Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such literary analyses remains a challenge in digital humanities. While most previous work focuses on"distant reading"by algorithmically discoveri...
{ "section_name": [ "Introduction", "Literary analyses of Invisible Cities", "A Computational Analysis", "Embedding city descriptions", "Clustering city representations", "Evaluating clustering assignments", "Quantitative comparison", "Examining the learned clusters", "Related work...
{ "question": [ "How do they model a city description using embeddings?", "How do they obtain human judgements?", "Which clustering method do they use to cluster city description embeddings?" ], "question_id": [ "508580af51483b5fb0df2630e8ea726ff08d537b", "89d1687270654979c53d0d0e6a845cdc89414...
{ "caption": [ "Figure 1: Calvino labels the thematically-similar cities in the top row as cities & the dead. However, although the bottom two cities share a theme of desire, he assigns them to different groups.", "Figure 2: We first embed each city by averaging token representations derived from a pretrained...
Introduction Literary critics form interpretations of meaning in works of literature. Building computational models that can help form and test these interpretations is a fundamental goal of digital humanities research BIBREF0 . Within natural language processing, most previous work that engages with literature relies ...
1909.00754
Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation
Existing approaches to dialogue state tracking rely on pre-defined ontologies consisting of a set of all possible slot types and values. Though such approaches exhibit promising performance on single-domain benchmarks, they suffer from computational complexity that increases proportionally to the number of pre-defined ...
{ "section_name": [ "Introduction", "Motivation", "Hierarchical Sequence Generation for DST", "Encoding Module", "Conditional Memory Relation Decoder", "Experimental Setting", "Implementation Details", "Results", "Ablation Study", "Qualitative Analysis", "Related Work", ...
{ "question": [ "Does this approach perform better in the multi-domain or single-domain setting?", "What are the performance metrics used?", "Which datasets are used to evaluate performance?" ], "question_id": [ "ed7a3e7fc1672f85a768613e7d1b419475950ab4", "72ceeb58e783e3981055c70a3483ea706511f...
{ "caption": [ "Table 1: The Inference Time Complexity (ITC) of previous DST models. The ITC is calculated based on how many times inference must be performed to complete a prediction of the belief state in a dialogue turn, where m is the number of values in a pre-defined ontology list and n is the number of slot...
Introduction A Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system BIBREF7 . For each dialogue turn, a DST module takes a user utterance and the dialogue history as input, and outputs a belief estimate of the dialogue state. Then a machine action is decided based on the dialogue ...
1906.00180
Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization
Can neural nets learn logic? We approach this classic question with current methods, and demonstrate that recurrent neural networks can learn to recognize first order logical entailment relations between expressions. We define an artificial language in first-order predicate logic, generate a large dataset of sample 'se...
{ "section_name": [ "Introduction & related work", "Task definition & data generation", "Learning models", "Results", "Zero-shot, compositional generalization", "Unseen lengths", "Unseen words", "Discussion & Conclusions" ], "paragraphs": [ [ "State-of-the-art models for ...
{ "question": [ "How does the automatic theorem prover infer the relation?", "If these model can learn the first-order logic on artificial language, why can't it lear for natural language?", "How many samples did they generate for the artificial language?" ], "question_id": [ "42812113ec720b560eb9...
{ "caption": [ "Figure 1: Venn diagrams visualizing the taxonomy of (a) nouns NL and (b) verbs VL in L.", "Table 3: FOL axiom representations of lexical entailment relations. For definition of relations, see Table 2.", "Figure 3: Visualization of the general recurrent model. The region in the dashed box r...
Introduction & related work State-of-the-art models for almost all popular natural language processing tasks are based on deep neural networks, trained on massive amounts of data. A key question that has been raised in many different forms is to what extent these models have learned the compositional generalizations th...
1806.02847
A Simple Method for Commonsense Reasoning
Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset~\cite{levesque2011winograd}. In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our m...
{ "section_name": [ "Introduction", "Related Work", "Methods", "Experimental settings", "Main results", "The first challenge in 2016: PDP-60", "Winograd Schema Challenge", "Customized training data for Winograd Schema Challenge", "Discovery of special words in Winograd Schema", ...
{ "question": [ "Which of their training domains improves performance the most?", "Do they fine-tune their model on the end task?" ], "question_id": [ "05bb75a1e1202850efa9191d6901de0a34744af0", "770aeff30846cd3d0d5963f527691f3685e8af02" ], "nlp_background": [ "infinity", "infinity" ...
{ "caption": [ "Figure 1: Overview of our method and analysis. We consider the test \"The trophy doesn’t fit in the suitcase because it is too big.\" Our method first substitutes two candidate references trophy and suitcase into the pronoun position. We then use an LM to score the resulting two substitutions. By ...
Introduction Although deep neural networks have achieved remarkable successes (e.g., BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 ), their dependence on supervised learning has been challenged as a significant weakness. Th...
1906.04571
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender stereotypes in English, the approaches that are commonly employed produce ungrammatical sentences in morphologically rich languages. We present...
{ "section_name": [ "Introduction", "Gender Stereotypes in Text", "A Markov Random Field for Morpho-Syntactic Agreement", "Parameterization", "Inference", "Parameter Estimation", "Intervention", "Experiments", "Intrinsic Evaluation", "Extrinsic Evaluation", "Related Work", ...
{ "question": [ "Why does not the approach from English work on other languages?", "How do they measure grammaticality?", "Which model do they use to convert between masculine-inflected and feminine-inflected sentences?" ], "question_id": [ "f7817b949605fb04b1e4fec9dd9ca8804fb92ae9", "8255f74c...
{ "caption": [ "Figure 1: Transformation of Los ingenieros son expertos (i.e., The male engineers are skilled) to Las ingenieras son expertas (i.e., The female engineers are skilled). We extract the properties of each word in the sentence. We then fix a noun and its tags and infer the manner in which the remainin...
Introduction One of the biggest challenges faced by modern natural language processing (NLP) systems is the inadvertent replication or amplification of societal biases. This is because NLP systems depend on language corpora, which are inherently “not objective; they are creations of human design” BIBREF0 . One type of ...
1909.04625
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
Neural language models have achieved state-of-the-art performances on many NLP tasks, and recently have been shown to learn a number of hierarchically-sensitive syntactic dependencies between individual words. However, equally important for language processing is the ability to combine words into phrasal constituents, ...
{ "section_name": [ "Introduction", "Methods ::: Psycholinguistics Paradigm", "Methods ::: Models Tested ::: Recurrent Neural Network (RNN) Language Models", "Methods ::: Models Tested ::: ActionLSTM", "Methods ::: Models Tested ::: Generative Recurrent Neural Network Grammars (RNNG)", "Experi...
{ "question": [ "What is the performance achieved by the model described in the paper?", "What is the best performance achieved by supervised models?", "What is the size of the datasets employed?", "What are the baseline models?" ], "question_id": [ "946676f1a836ea2d6fe98cb4cfc26b9f4f81984d", ...
{ "caption": [ "Figure 1: Subject-verb agreement with (a) the head of a noun phrase structure, and (b) the coordination structure.", "Table 1: A summary of models tested.", "Table 2: Conditions of number agreement in Noncoordination Agreement experiment.", "Table 3: Conditions of gender agreement in N...
Introduction Humans deploy structure-sensitive expectations to guide processing during natural language comprehension BIBREF0. While it has been shown that neural language models show similar structure-sensitivity in their predictions about upcoming material BIBREF1, BIBREF2, previous work has focused on dependencies t...
1809.07629
Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
Natural language generation (NLG) is a critical component in spoken dialogue system, which can be divided into two phases: (1) sentence planning: deciding the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string. With the rise of deep l...
{ "section_name": [ "Introduction", "Hierarchical Natural Language Generation (HNLG)", "Attentional Hierarchical Decoder", "Scheduled Sampling", "Curriculum Learning", "Repeat-Input Mechanism", "Attention Mechanism", "Training", "Setup", "Results and Analysis", "Conclusion"...
{ "question": [ "What evaluation metrics are used?", "What datasets did they use?" ], "question_id": [ "c5171daf82107fce0f285fa18f19e91fbd1215c5", "baeb6785077931e842079e9d0c9c9040947ffa4e" ], "nlp_background": [ "", "" ], "topic_background": [ "", "" ], "paper_read": [...
{ "caption": [ "Fig. 1. The illustration of the proposed semantically conditioned NLG model. The hierarchical decoder contains four decoder layer, each is only responsible for learning to insert words of a specific set of POS tags into the sequence.", "Table 1. The proposed attentional hierarchical NLG models...
Introduction Spoken dialogue systems that can help users to solve complex tasks have become an emerging research topic in artificial intelligence and natural language processing areas BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . With a well-designed dialogue system as an intelligent personal assistant, people can accomplish...
1807.05154
Deep Enhanced Representation for Implicit Discourse Relation Recognition
Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs. Thus, properly representing the text is very crucial to this task...
{ "section_name": [ "Introduction", "Related Work", "Overview", "Word-Level Module", "Sentence-Level Module", "Pair-Level Module", "Classifier", "ExperimentsThe code for this paper is available at https://github.com/diccooo/Deep_Enhanced_Repr_for_IDRR", "11-way Classification", ...
{ "question": [ "Why does their model do better than prior models?" ], "question_id": [ "bb570d4a1b814f508a07e74baac735bf6ca0f040" ], "nlp_background": [ "infinity" ], "topic_background": [ "familiar" ], "paper_read": [ "no" ], "search_query": [ "" ], "question_writer":...
{ "caption": [ "Figure 1: Model overview.", "Figure 2: Subword encoder.", "Figure 4: Recurrent encoder block.", "Table 1: Shared hyperparameter settings. Before dimension reducing, the dimension of pre-trained ELMo embedding is 1024.", "Table 2: Accuracy (%) comparison with others’ results on PDTB...
Introduction This work is licenced under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/ Discourse parsing is a fundamental task in natural language processing (NLP) which determines the structure of the whole discourse and identifies the relation...
2002.11402
Detecting Potential Topics In News Using BERT, CRF and Wikipedia
For a news content distribution platform like Dailyhunt, Named Entity Recognition is a pivotal task for building better user recommendation and notification algorithms. Apart from identifying names, locations, organisations from the news for 13+ Indian languages and use them in algorithms, we also need to identify n-gr...
{ "section_name": [ "Introduction & Related Work", "Data Preparation", "Experiments ::: Model Architecture", "Experiments ::: Training", "Experiments ::: Results", "Experiments ::: Discussions", "Conclusion and Future Work" ], "paragraphs": [ [ "Named-Entity-Recognition(NER) ...
{ "question": [ "What is the difference in recall score between the systems?", "What is their f1 score and recall?", "How many layers does their system have?", "Which news corpus is used?", "How large is the dataset they used?" ], "question_id": [ "1771a55236823ed44d3ee537de2e85465bf03eaf"...
{ "caption": [ "Table 1. Parallel Corpus Preparation with BERT Tokenizer", "Table 2. Comparison with Traditional NERs as reference", "Table 3. Comparison with Wikipedia titles as reference", "Figure 1. BERT + Bi-GRU + CRF, Final Architecture Chosen For Topic Detection Task.", "Table 4. Recognised ...
Introduction & Related Work Named-Entity-Recognition(NER) approaches can be categorised broadly in three types. Detecting NER with predefined dictionaries and rulesBIBREF2, with some statistical approachesBIBREF3 and with deep learning approachesBIBREF4. Stanford CoreNLP NER is a widely used baseline for many applicat...
1804.09301
Gender Bias in Coreference Resolution
We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these"Winogender schemas,"we evaluate and confirm systematic gender bias in three publicly-available coreference resolu...
{ "section_name": [ "Introduction", "Coreference Systems", "Winogender Schemas", "Results and Discussion", "Related Work", "Conclusion and Future Work", "Acknowledgments" ], "paragraphs": [ [ "There is a classic riddle: A man and his son get into a terrible car crash. The fat...
{ "question": [ "Which coreference resolution systems are tested?" ], "question_id": [ "c2553166463b7b5ae4d9786f0446eb06a90af458" ], "nlp_background": [ "infinity" ], "topic_background": [ "research" ], "paper_read": [ "yes" ], "search_query": [ "gender bias" ], "questi...
{ "caption": [ "Figure 1: Stanford CoreNLP rule-based coreference system resolves a male and neutral pronoun as coreferent with “The surgeon,” but does not for the corresponding female pronoun.", "Figure 2: A “Winogender” schema for the occupation paramedic. Correct answers in bold. In general, OCCUPATION and...
Introduction There is a classic riddle: A man and his son get into a terrible car crash. The father dies, and the boy is badly injured. In the hospital, the surgeon looks at the patient and exclaims, “I can't operate on this boy, he's my son!” How can this be? That a majority of people are reportedly unable to solve t...
2002.00652
How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context
Recently semantic parsing in context has received a considerable attention, which is challenging since there are complex contextual phenomena. Previous works verified their proposed methods in limited scenarios, which motivates us to conduct an exploratory study on context modeling methods under real-world semantic par...
{ "section_name": [ "Introduction", "Methodology", "Methodology ::: Base Model", "Methodology ::: Base Model ::: Question Encoder", "Methodology ::: Base Model ::: Grammar-based Decoder", "Methodology ::: Recent Questions as Context", "Methodology ::: Recent Questions as Context ::: Concat...
{ "question": [ "How big is improvement in performances of proposed model over state of the art?", "What two large datasets are used for evaluation?", "What context modelling methods are evaluated?", "What are two datasets models are tested on?" ], "question_id": [ "cc9f0ac8ead575a9b485a51ddc0...
{ "caption": [ "Figure 1: An example dialogue (right) and its database schema (left).", "Figure 2: The grammar rule and the abstract syntax tree for the SQL", "Figure 3: Different methods to incorporate recent h questions [xi−h, ...,xi−1]. (a) CONCAT: concatenate recent questions with xi as input; (b) TUR...
Introduction Semantic parsing, which translates a natural language sentence into its corresponding executable logic form (e.g. Structured Query Language, SQL), relieves users from the burden of learning techniques behind the logic form. The majority of previous studies on semantic parsing assume that queries are contex...
1909.00324
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis
Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence representation generation. In this paper, we propose a novel Aspect-Guided Deep Transition model,...
{ "section_name": [ "Introduction", "Model Description", "Model Description ::: Aspect-Guided Encoder", "Model Description ::: Aspect-Reconstruction", "Model Description ::: Training Objective", "Experiments ::: Datasets and Metrics ::: Data Preparation.", "Experiments ::: Datasets and Met...
{ "question": [ "How big is the improvement over the state-of-the-art results?", "Is the model evaluated against other Aspect-Based models?" ], "question_id": [ "1763a029daca7cab10f18634aba02a6bd1b6faa7", "f9de9ddea0c70630b360167354004ab8cbfff041" ], "nlp_background": [ "two", "two" ...
{ "caption": [ "Table 1: The instance contains different sentiment polarities towards two aspects.", "Figure 1: The overview of AGDT. The bottom right dark node (above the aspect embedding) is the aspect gate and other dark nodes (⊗) means element-wise multiply for the input token and the aspect gate. The asp...
Introduction Aspect based sentiment analysis (ABSA) is a fine-grained task in sentiment analysis, which can provide important sentiment information for other natural language processing (NLP) tasks. There are two different subtasks in ABSA, namely, aspect-category sentiment analysis and aspect-term sentiment analysis B...
1905.06566
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization
Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these \emph{inaccurate} labels is challenging. Inspired by the recent work...
{ "section_name": [ "Introduction", "Related Work", "Model", "Document Representation", "Pre-training", "Extractive Summarization", "Experiments", "Datasets", "Implementation Details", "Evaluations", "Results", "Conclusions" ], "paragraphs": [ [ "Automatic...
{ "question": [ "Is the baseline a non-heirarchical model like BERT?" ], "question_id": [ "fc8bc6a3c837a9d1c869b7ee90cf4e3c39bcd102" ], "nlp_background": [ "five" ], "topic_background": [ "familiar" ], "paper_read": [ "no" ], "search_query": [ "transformers" ], "questio...
{ "caption": [ "Figure 1: The architecture of HIBERT during training. senti is a sentence in the document above, which has four sentences in total. sent3 is masked during encoding and the decoder predicts the original sent3.", "Figure 2: The architecture of our extractive summarization model. The sentence and...
Introduction Automatic document summarization is the task of rewriting a document into its shorter form while still retaining its important content. Over the years, many paradigms for document summarization have been explored (see Nenkova:McKeown:2011 for an overview). The most popular two among them are extractive app...
2003.04032
Shallow Discourse Annotation for Chinese TED Talks
Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English translation, in the form of a set of TED talks (some originally given in English, some in Chin...
{ "section_name": [ "Introduction", "Related work", "PDTB and our Annotation Scheme", "PDTB and our Annotation Scheme ::: Arguments", "PDTB and our Annotation Scheme ::: Relations", "PDTB and our Annotation Scheme ::: Senses", "Annotation Procedure", "Annotation Procedure ::: Annotator...
{ "question": [ "Do they build a model to recognize discourse relations on their dataset?", "Which inter-annotator metric do they use?", "How high is the inter-annotator agreement?", "How are resources adapted to properties of Chinese text?" ], "question_id": [ "58e65741184c81c9e7fe0ca15832df2...
{ "caption": [ "Table 1: PDTB-3 Sense Hierarchy (Webber et al., 2019)", "Table 2: The length and the number of relations of each text", "Table 3: Agreement study", "Figure 1: Relation distribution", "Table 4: Disagreements between annotators: Percentage of cases", "Table 5: Distribution of cla...
Introduction Researchers have recognized that performance improvements in natural language processing (NLP) tasks such as summarization BIBREF0, question answering BIBREF1, and machine translation BIBREF2 can come from recognizing discourse-level properties of text. These include properties such as the how new entiti...
2004.03034
The Role of Pragmatic and Discourse Context in Determining Argument Impact
Research in the social sciences and psychology has shown that the persuasiveness of an argument depends not only the language employed, but also on attributes of the source/communicator, the audience, and the appropriateness and strength of the argument's claims given the pragmatic and discourse context of the argument...
{ "section_name": [ "Introduction", "Related Work", "Dataset", "Methodology ::: Hypothesis and Task Description", "Methodology ::: Baseline Models ::: Majority", "Methodology ::: Baseline Models ::: SVM with RBF kernel", "Methodology ::: Baseline Models ::: FastText", "Methodology ::: ...
{ "question": [ "How better are results compared to baseline models?", "What models that rely only on claim-specific linguistic features are used as baselines?", "How is pargmative and discourse context added to the dataset?", "What annotations are available in the dataset?" ], "question_id": [ ...
{ "caption": [ "Figure 1: Example partial argument tree with claims and corresponding impact votes for the thesis “PHYSICAL TORTURE OF PRISONERS IS AN ACCEPTABLE INTERROGATION TOOL.”.", "Table 1: Number of claims for the given range of number of votes. There are 19,512 claims in the dataset with 3 or more vot...
Introduction Previous work in the social sciences and psychology has shown that the impact and persuasive power of an argument depends not only on the language employed, but also on the credibility and character of the communicator (i.e. ethos) BIBREF0, BIBREF1, BIBREF2; the traits and prior beliefs of the audience BIB...
1910.12618
Textual Data for Time Series Forecasting
While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, openly accessible daily weather reports from France and the United-Kingdom are leveraged to pr...
{ "section_name": [ "Introduction", "Presentation of the data", "Presentation of the data ::: Time Series", "Presentation of the data ::: Text", "Modeling and forecasting framework", "Modeling and forecasting framework ::: Numerical Encoding of the Text", "Modeling and forecasting framewor...
{ "question": [ "How big is dataset used for training/testing?", "Is there any example where geometric property is visible for context similarity between words?", "What geometric properties do embeddings display?", "How accurate is model trained on text exclusively?" ], "question_id": [ "07c59...
{ "caption": [ "Figure 1: Net electricity consumption (Load) over time.", "Figure 2: Word counts for the two corpora after preprocessing.", "Table 3: Descriptive analysis of the two corpora (after preprocessing)", "Figure 3: Structure of our RNN. Dropout and batch normalization are not represented.", ...
Introduction Whether it is in the field of energy, finance or meteorology, accurately predicting the behavior of time series is nowadays of paramount importance for optimal decision making or profit. While the field of time series forecasting is extremely prolific from a research point-of-view, up to now it has narrowe...
1911.12569
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses Distributional Thesaurus as a source of external knowledge to improve the sentiment and emotion pre...
{ "section_name": [ "Introduction", "Related Work", "Proposed Methodology", "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: BiLSTM based word encoder", "Proposed Methodology ::: Two-Layered Multi-Task Attention Model ::: Word Attention", "Proposed Methodology ::: Two-Layer...
{ "question": [ "What was their result on Stance Sentiment Emotion Corpus?", "What performance did they obtain on the SemEval dataset?", "What are the state-of-the-art systems?", "How is multi-tasking performed?", "What are the datasets used for training?", "How many parameters does the model ...
{ "caption": [ "Fig. 1. Two-layered multi-task attention based network", "TABLE I DATASET STATISTICS OF SEMEVAL 2016 TASK 6 AND SSEC USED FOR SENTIMENT AND EMOTION ANALYSIS, RESPECTIVELY.", "TABLE II F-SCORE OF VARIOUS MODELS ON SENTIMENT AND EMOTION TEST DATASET.", "TABLE III COMPARISON WITH THE STAT...
Introduction The emergence of social media sites with limited character constraint has ushered in a new style of communication. Twitter users within 280 characters per tweet share meaningful and informative messages. These short messages have a powerful impact on how we perceive and interact with other human beings. Th...
1910.01363
Mapping (Dis-)Information Flow about the MH17 Plane Crash
Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets...
{ "section_name": [ "Introduction", "Introduction ::: MH17 Related (Dis-)Information Flow on Twitter", "Introduction ::: Contributions", "Competing Narratives about the MH17 Crash", "Dataset", "Classification Models", "Classification Models ::: Hashtag-Based Baseline", "Classification ...
{ "question": [ "How can the classifier facilitate the annotation task for human annotators?", "What recommendations are made to improve the performance in future?", "What type of errors do the classifiers use?", "What neural classifiers are used?", "What is the hashtags does the hashtag-based bas...
{ "caption": [ "Table 1: Label distribution and dataset sizes. Tweets are considered original if their preprocessed text is unique. All tweets comprise original tweets, retweets and duplicates.", "Table 2: Example tweets for each of the three classes.", "Table 3: Classification results on the English MH17...
Introduction Digital media enables fast sharing of information, including various forms of false or deceptive information. Hence, besides bringing the obvious advantage of broadening information access for everyone, digital media can also be misused for campaigns that spread disinformation about specific events, or cam...
1901.04899
Conversational Intent Understanding for Passengers in Autonomous Vehicles
Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automated-vehicle Multimodal In-cabin Ex...
{ "section_name": [ "Introduction", "Methodology", "Experimental Results", "Conclusion" ], "paragraphs": [ [ "Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehic...
{ "question": [ "What are the supported natural commands?", "What is the size of their collected dataset?", "Did they compare against other systems?", "What intents does the paper explore?" ], "question_id": [ "c6e63e3b807474e29bfe32542321d015009e7148", "4ef2fd79d598accc54c084f0cca8ad7c1b3...
{ "caption": [ "Table 1: Slot Extraction Results (10-fold CV)", "Table 3: Utterance-level Intent Recognition Results (10-fold CV)", "Table 2: Intent Keyword Extraction Results (10-fold CV)", "Table 4: Intent-wise Performance Results of Utterance-level Intent Recognition Models: Hierarchical & Joint (1...
Introduction Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automated-vehicle Multimoda...
1606.05320
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions. We focus on recurrent neural networks (RNNs), state of the art models in speech re...
{ "section_name": [ "Introduction", "Methods", "LSTM models", "Hidden Markov models", "Hybrid models", "Experiments", "Conclusion and future work" ], "paragraphs": [ [ "Following the recent progress in deep learning, researchers and practitioners of machine learning are recog...
{ "question": [ "What kind of features are used by the HMM models, and how interpretable are those?", "What kind of information do the HMMs learn that the LSTMs don't?", "Which methods do the authors use to reach the conclusion that LSTMs and HMMs learn complementary information?", "How large is the g...
{ "caption": [ "Figure 1: Hybrid HMM-LSTM algorithms (the dashed blocks indicate the components trained using SGD in Torch).", "Table 1: Predictive loglikelihood (LL) comparison, sorted by validation set performance.", "Figure 2: Visualizing HMM and LSTM states on Linux data for the hybrid with 10 LSTM st...
Introduction Following the recent progress in deep learning, researchers and practitioners of machine learning are recognizing the importance of understanding and interpreting what goes on inside these black box models. Recurrent neural networks have recently revolutionized speech recognition and translation, and these...
1809.10644
Predictive Embeddings for Hate Speech Detection on Twitter
We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on thr...
{ "section_name": [ "Introduction", "Related Work", "Data", "Transformed Word Embedding Model (TWEM)", "Word Embeddings", "Pooling", "Output", "Experimental Setup", "Results and Discussion", "Error Analysis", "Conclusion", "Supplemental Material", "Preprocessing", ...
{ "question": [ "Do they report results only on English data?", "Which publicly available datasets are used?", "What embedding algorithm and dimension size are used?", "What data are the embeddings trained on?", "how much was the parameter difference between their model and previous methods?", ...
{ "caption": [ "Table 1: Dataset Characteristics", "Table 2: F1 Results3", "Table 3: Projected Embedding Cluster Analysis from SR Dataset", "Table 5: SR Results", "Table 7: HAR Results", "Table 6: HATE Results", "Table 8: Projected Embedding Cluster Analysis from SR Dataset" ], "file":...
Introduction The increasing popularity of social media platforms like Twitter for both personal and political communication BIBREF0 has seen a well-acknowledged rise in the presence of toxic and abusive speech on these platforms BIBREF1 , BIBREF2 . Although the terms of services on these platforms typically forbid hate...
1606.02006
Incorporating Discrete Translation Lexicons into Neural Machine Translation
Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence. We propose a method to alleviate this problem by augmenting NMT systems with discrete translation lexicons that efficiently encode translations of these low-f...
{ "section_name": [ "Introduction", "Neural Machine Translation", "Integrating Lexicons into NMT", "Converting Lexicon Probabilities into Conditioned Predictive Proabilities", "Combining Predictive Probabilities", "Constructing Lexicon Probabilities", "Automatically Learned Lexicons", ...
{ "question": [ "What datasets were used?", "What language pairs did they experiment with?" ], "question_id": [ "102a0439739428aac80ac11795e73ce751b93ea1", "d9c26c1bfb3830c9f3dbcccf4c8ecbcd3cb54404" ], "nlp_background": [ "", "" ], "topic_background": [ "", "" ], "paper...
{ "caption": [ "Figure 1: An example of a mistake made by NMT on low-frequency content words.", "Table 1: Corpus details.", "Table 2: Accuracies for the baseline attentional NMT (attn) and the proposed bias-based method using the automatic (auto-bias) or hybrid (hyb-bias) dictionaries. Bold indicates a ga...
Introduction Neural machine translation (NMT, § SECREF2 ; kalchbrenner13emnlp, sutskever14nips) is a variant of statistical machine translation (SMT; brown93cl), using neural networks. NMT has recently gained popularity due to its ability to model the translation process end-to-end using a single probabilistic model, a...
1911.03243
Crowdsourcing a High-Quality Gold Standard for QA-SRL
Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed as an attractive open and natural form of SRL, easily crowdsourceable for new corpora. Recently, a large-scale QA-SRL corpus and a trained parser were released, accompanied by a densely annotated dataset for evaluation. Trying to replicate the QA-...
{ "section_name": [ "Introduction", "Background — QA-SRL ::: Specifications", "Background — QA-SRL ::: Corpora", "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Screening and Training", "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Annotation", "Anno...
{ "question": [ "How much more coverage is in the new dataset?", "How was coverage measured?", "How was quality measured?", "How was the corpus obtained?", "How are workers trained?", "What is different in the improved annotation protocol?", "How was the previous dataset annotated?", "...
{ "caption": [ "Table 1: Running examples of QA-SRL annotations; this set is a sample of the possible questions that can be asked. The bar (|) separates multiple selected answers.", "Table 2: Automatic and manually-corrected evaluation of our gold standard and Dense (Fitzgerald et al., 2018) against the exper...
Introduction Semantic Role Labeling (SRL) provides explicit annotation of predicate-argument relations, which have been found useful in various downstream tasks BIBREF0, BIBREF1, BIBREF2, BIBREF3. Question-Answer driven Semantic Role Labeling (QA-SRL) BIBREF4 is an SRL scheme in which roles are captured by natural lang...
1809.04686
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation
Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems that can transla...
{ "section_name": [ "Introduction", "Proposed Method", "Multilingual Representations Using NMT", "Multilingual Encoder-Classifier", "Corpora", "Model and Training Details", "Transfer Learning Results", "Zero-Shot Classification Results", "Analyses", "Conclusion" ], "paragra...
{ "question": [ "Do the other multilingual baselines make use of the same amount of training data?", "How big is the impact of training data size on the performance of the multilingual encoder?", "What data were they used to train the multilingual encoder?" ], "question_id": [ "05196588320dfb0b9d9...
{ "caption": [ "Table 1: Transfer learning results of the classification accuracy on all the datasets. Amazon (En) and Amazon (Fr) are the English and French versions of the task, training the models on the data for each language. The state-of-the-art results are cited from Fernndez, Esuli, and Sebastiani (2016) ...
Introduction Transfer learning has been shown to work well in Computer Vision where pre-trained components from a model trained on ImageNet BIBREF0 are used to initialize models for other tasks BIBREF1 . In most cases, the other tasks are related to and share architectural components with the ImageNet task, enabling th...
1703.09684
An Analysis of Visual Question Answering Algorithms
In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 2014, they all have flaws in both their content and the way algorithms are evaluated on them. As a result, evaluation scores are inflated and predominantly determi...
{ "section_name": [ "Introduction", "Prior Natural Image VQA Datasets", "Synthetic Datasets that Fight Bias", "TDIUC for Nuanced VQA Analysis", "Importing Questions from Existing Datasets", "Generating Questions using Image Annotations", "Manual Annotation", "Post Processing", "Pro...
{ "question": [ "From when are many VQA datasets collected?" ], "question_id": [ "cf93a209c8001ffb4ef505d306b6ced5936c6b63" ], "nlp_background": [ "five" ], "topic_background": [ "familiar" ], "paper_read": [ "no" ], "search_query": [ "Question Answering" ], "question_w...
{ "caption": [ "Figure 1: A good VQA benchmark tests a wide range of computer vision tasks in an unbiased manner. In this paper, we propose a new dataset with 12 distinct tasks and evaluation metrics that compensate for bias, so that the strengths and limitations of algorithms can be better measured.", "Figur...
Introduction In open-ended visual question answering (VQA) an algorithm must produce answers to arbitrary text-based questions about images BIBREF0 , BIBREF1 . VQA is an exciting computer vision problem that requires a system to be capable of many tasks. Truly solving VQA would be a milestone in artificial intelligence...
1911.11744
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration
In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn is used to synthesize specific motion controllers at run-time. This multimodal approach enables generalization to a wide v...
{ "section_name": [ "Introduction", "Introduction ::: Problem Statement:", "Background", "Multimodal Policy Generation via Imitation", "Results", "Conclusion and Future Work" ], "paragraphs": [ [ "A significant challenge when designing robots to operate in the real world lies in ...
{ "question": [ "What is task success rate achieved? ", "What simulations are performed by the authors to validate their approach?", "Does proposed end-to-end approach learn in reinforcement or supervised learning manner?" ], "question_id": [ "fb5ce11bfd74e9d7c322444b006a27f2ff32a0cf", "1e2ffa...
{ "caption": [ "Figure 1: Network architecture overview. The network consists of two parts, a high-level semantic network and a low-level control network. Both networks are working seamlessly together and are utilized in an End-to-End fashion.", "Figure 2: Results for placing an object into bowls at different...
Introduction A significant challenge when designing robots to operate in the real world lies in the generation of control policies that can adapt to changing environments. Programming such policies is a labor and time-consuming process which requires substantial technical expertise. Imitation learning BIBREF0, is an ap...
1910.03467
Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation
Among the six challenges of neural machine translation (NMT) coined by (Koehn and Knowles, 2017), rare-word problem is considered the most severe one, especially in translation of low-resource languages. In this paper, we propose three solutions to address the rare words in neural machine translation systems. First, we...
{ "section_name": [ "Introduction", "Neural Machine Translation", "Rare Word translation", "Rare Word translation ::: Low-frequency Word Translation", "Rare Word translation ::: Reducing Unknown Words", "Rare Word translation ::: Dealing with OOV using WordNet", "Experiments", "Experim...
{ "question": [ "Are synonymous relation taken into account in the Japanese-Vietnamese task?", "Is the supervised morphological learner tested on Japanese?" ], "question_id": [ "b367b823c5db4543ac421d0057b02f62ea16bf9f", "84737d871bde8058d8033e496179f7daec31c2d3" ], "nlp_background": [ "tw...
{ "caption": [ "Table 1: Results of Japanese-Vietnamese NMT systems", "Table 2: The number of Japanese OOV words replaced by their synonyms.", "Table 3: Results of English-Vietnamese NMT systems", "Table 4: The number of rare words in which their affixes are detached from the English texts in the SAA ...
Introduction NMT systems have achieved better performance compared to statistical machine translation (SMT) systems in recent years not only on available data language pairs BIBREF1, BIBREF2, but also on low-resource language pairs BIBREF3, BIBREF4. Nevertheless, NMT still exists many challenges which have adverse effe...
1908.09156
A framework for anomaly detection using language modeling, and its applications to finance
In the finance sector, studies focused on anomaly detection are often associated with time-series and transactional data analytics. In this paper, we lay out the opportunities for applying anomaly and deviation detection methods to text corpora and challenges associated with them. We argue that language models that use...
{ "section_name": [ "Introduction", "Five views on anomaly", "Five views on anomaly ::: Anomaly as error", "Five views on anomaly ::: Anomaly as irregularity", "Five views on anomaly ::: Anomaly as novelty", "Five views on anomaly ::: Anomaly as semantic richness", "Five views on anomaly :...
{ "question": [ "What is the dataset that is used in the paper?", "What is the performance of the models discussed in the paper?", "Does the paper consider the use of perplexity in order to identify text anomalies?", "Does the paper report a baseline for the task?" ], "question_id": [ "7b3d207...
{ "caption": [ "Figure 1: Illustration of a recurrent step in a languagemodel. Excerpted from [8].", "Figure 2: A pre-trained model can be fine-tuned on a new domain, and applied to a classification or prediction task. Excerpted from [6].", "Table 1: Four scenarios for anomaly detection on text data using...
Introduction The detection of anomalous trends in the financial domain has focused largely on fraud detection BIBREF0, risk modeling BIBREF1, and predictive analysis BIBREF2. The data used in the majority of such studies is of time-series, transactional, graph or generally quantitative or structured nature. This belies...
1911.00523
What Gets Echoed? Understanding the"Pointers"in Explanations of Persuasive Arguments
Explanations are central to everyday life, and are a topic of growing interest in the AI community. To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView subreddit to build a dataset with 36K naturally occurring explanations of why an argument is persuasi...
{ "section_name": [ "Introduction", "Related Work", "Dataset", "Understanding the Pointers in Explanations", "Predicting Pointers", "Predicting Pointers ::: Experiment setup", "Predicting Pointers ::: Prediction Performance", "Predicting Pointers ::: The Effect on Generating Explanatio...
{ "question": [ "What non-contextual properties do they refer to?", "What is the baseline?", "What are their proposed features?", "What are overall baseline results on new this new task?", "What metrics are used in evaluation of this task?", "Do authors provide any explanation for intriguing p...
{ "caption": [ "Table 1: Sample data that were affected by preprocessing.", "Figure 1: The U-shape exists both in Figure 1a and Figure 1b, but not in Figure 1c.", "Table 2: Full testing results after Bonferroni correction.", "Table 3: Feature importance for the full XGBoost model, as measured by total...
Introduction Explanations are essential for understanding and learning BIBREF0. They can take many forms, ranging from everyday explanations for questions such as why one likes Star Wars, to sophisticated formalization in the philosophy of science BIBREF1, to simply highlighting features in recent work on interpretable...
1803.03664
Automating Reading Comprehension by Generating Question and Answer Pairs
Neural network-based methods represent the state-of-the-art in question generation from text. Existing work focuses on generating only questions from text without concerning itself with answer generation. Moreover, our analysis shows that handling rare words and generating the most appropriate question given a candidat...
{ "section_name": [ "Introduction", "Problem Formulation", "Related Work", "Approach and Contributions", "Answer Selection and Encoding", "Named Entity Selection", "Answer Selection using Pointer Networks", "Question Generation", "Sequence to Sequence Model", "Linguistic Featur...
{ "question": [ "Which datasets are used to train this model?" ], "question_id": [ "2b78052314cb730824836ea69bc968df7964b4e4" ], "nlp_background": [ "five" ], "topic_background": [ "familiar" ], "paper_read": [ "no" ], "search_query": [ "question" ], "question_writer": ...
{ "caption": [ "Table 1: Human evaluation results on Ste. Parameters are, p1: percentage of syntactically correct questions, p2: percentage of semantically correct questions, p3: percentage of relevant questions.", "Table 2: Automatic evaluation results on Ste. BLEU, METEOR and ROUGE-L scores vary between 0 a...
Introduction Asking relevant and intelligent questions has always been an integral part of human learning, as it can help assess the user's understanding of a piece of text (an article, an essay etc.). However, forming questions manually can be sometimes arduous. Automated question generation (QG) systems can help alle...
1910.11949
Automatic Reminiscence Therapy for Dementia.
With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapie...
{ "section_name": [ "Introduction", "Related Work", "Methodology", "Methodology ::: VQG model", "Methodology ::: Chatbot network", "Datasets", "Datasets ::: MS-COCO, Bing and Flickr datasets", "Datasets ::: Persona-chat and Cornell-movie corpus", "Validation", "Validation ::: I...
{ "question": [ "How is performance of this system measured?", "How many questions per image on average are available in dataset?", "Is machine learning system underneath similar to image caption ML systems?", "How big dataset is used for training this system?" ], "question_id": [ "11d2f0d913d...
{ "caption": [ "Figure 1: Scheme of the interaction with Elisabot", "Figure 2: Samples from Bing 2a), Coco 2b) and Flickr 2c) datasets", "Table 1: Generated questions", "Figure 3: Elisabot running on Telegram application", "Figure 5: Sample of the session study with mild cognitive impairment patie...
Introduction Increases in life expectancy in the last century have resulted in a large number of people living to old ages and will result in a double number of dementia cases by the middle of the century BIBREF0BIBREF1. The most common form of dementia is Alzheimer disease which contributes to 60–70% of cases BIBREF2....
1902.09087
Lattice CNNs for Matching Based Chinese Question Answering
Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly. In this paper, we propose a novel lattice based CNN model (LCNs) to u...
{ "section_name": [ "Introduction", "Lattice CNNs", "Siamese Architecture", "Word Lattice", "Lattice based CNN Layer", "Experiments", "Datasets", "Evaluation Metrics", "Implementation Details", "Baselines", "Results", "Analysis and Discussions", "Case Study", "R...
{ "question": [ "How do they obtain word lattices from words?", "Which metrics do they use to evaluate matching?", "Which dataset(s) do they evaluate on?" ], "question_id": [ "76377e5bb7d0a374b0aefc54697ac9cd89d2eba8", "85aa125b3a15bbb6f99f91656ca2763e8fbdb0ff", "4b128f9e94d242a8e926bdcb24...
{ "caption": [ "Figure 1: A word lattice for the phrase “Chinese people have high quality of life.”", "Figure 2: An illustration of our LCN-gated, when “人民” (people) is being considered as the center of convolutional spans.", "Table 1: The performance of all models on the two datasets. The best results in...
Introduction Short text matching plays a critical role in many natural language processing tasks, such as question answering, information retrieval, and so on. However, matching text sequences for Chinese or similar languages often suffers from word segmentation, where there are often no perfect Chinese word segmentati...
2003.04748
On the coexistence of competing languages
We investigate the evolution of competing languages, a subject where much previous literature suggests that the outcome is always the domination of one language over all the others. Since coexistence of languages is observed in reality, we here revisit the question of language competition, with an emphasis on uncoverin...
{ "section_name": [ "Introduction", "Breaking internal symmetry: language coexistence by imbalanced population dynamics", "Breaking internal symmetry: language coexistence by imbalanced population dynamics ::: Two competing languages", "Breaking internal symmetry: language coexistence by imbalanced po...
{ "question": [ "What languages do they look at?" ], "question_id": [ "f8f13576115992b0abb897ced185a4f9d35c5de9" ], "nlp_background": [ "two" ], "topic_background": [ "unfamiliar" ], "paper_read": [ "no" ], "search_query": [ "" ], "question_writer": [ "c1fbdd7a26102...
{ "caption": [ "Fig. 1. Phase diagram of the model in the q–C plane. I: consensus phase. II: coexistence phase.", "Fig. 3. Steady state for 5 competing languages with equally spaced attractivenesses. The fractions xi of speakers of surviving languages are plotted against the mean attractiveness g in each sect...
Introduction The dynamics of language evolution is one of many interdisciplinary fields to which methods and insights from statistical physics have been successfully applied (see BIBREF0 for an overview, and BIBREF1 for a specific comprehensive review). In this work we revisit the question of language coexistence. It ...
1907.01413
Speaker-independent classification of phonetic segments from raw ultrasound in child speech
Ultrasound tongue imaging (UTI) provides a convenient way to visualize the vocal tract during speech production. UTI is increasingly being used for speech therapy, making it important to develop automatic methods to assist various time-consuming manual tasks currently performed by speech therapists. A key challenge is ...
{ "section_name": [ "Introduction", "Ultrasound Tongue Imaging", "Related Work", "Ultrasound Data", "Data Selection", "Preprocessing and Model Architectures", "Training Scenarios and Speaker Means", "Results and Discussion", "Future Work", "Conclusion" ], "paragraphs": [ ...
{ "question": [ "Do they report results only on English data?", "Do they propose any further additions that could be made to improve generalisation to unseen speakers?", "What are the characteristics of the dataset?", "What type of models are used for classification?", "Do they compare to previous...
{ "caption": [ "Fig. 1. Ultrasound samples for the four output classes based on place of articulation. The top row contains samples from speaker 12 (male, aged six), and the bottom row from speaker 13 (female, aged eleven). All samples show a midsaggital view of the oral cavity with the tip of the tongue facing r...
Introduction Ultrasound tongue imaging (UTI) uses standard medical ultrasound to visualize the tongue surface during speech production. It provides a non-invasive, clinically safe, and increasingly inexpensive method to visualize the vocal tract. Articulatory visual biofeedback of the speech production process, using U...
1908.07816
A Multi-Turn Emotionally Engaging Dialog Model
Open-domain dialog systems (also known as chatbots) have increasingly drawn attention in natural language processing. Some of the recent work aims at incorporating affect information into sequence-to-sequence neural dialog modeling, making the response emotionally richer, while others use hand-crafted rules to determin...
{ "section_name": [ "Introduction", "Related Work", "Model", "Model ::: Hierarchical Attention", "Model ::: Emotion Encoder", "Model ::: Decoding", "Evaluation", "Evaluation ::: Datasets", "Evaluation ::: Baselines and Implementation", "Evaluation ::: Evaluation Metrics", "...
{ "question": [ "How better is proposed method than baselines perpexity wise?", "How does the multi-turn dialog system learns?", "How is human evaluation performed?", "Is some other metrics other then perplexity measured?", "What two baseline models are used?" ], "question_id": [ "c034f38a...
{ "caption": [ "Figure 1: The overall architecture of our model.", "Table 1: Statistics of the two datasets.", "Table 2: Perplexity scores achieved by the models. Validation set 1 comes from the Cornell dataset, while validation set 2 comes from the DailyDialog dataset.", "Table 5: Human evaluation re...
Introduction Recent development in neural language modeling has generated significant excitement in the open-domain dialog generation community. The success of sequence-to-sequence learning BIBREF0, BIBREF1 in the field of neural machine translation has inspired researchers to apply the recurrent neural network (RNN) e...
1703.03097
Information Extraction in Illicit Domains
Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have `long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, ...
{ "section_name": [ "Introduction", "Related Work", "Approach", "Preprocessing", "Deriving Word Representations", "Applying High-Recall Recognizers", "Supervised Contextual Classifier", "Datasets and Ground-truths", "System", "Baselines", "Setup and Parameters", "Result...
{ "question": [ "Do they evaluate on relation extraction?" ], "question_id": [ "fb3687ea05d38b5e65fdbbbd1572eacd82f56c0b" ], "nlp_background": [ "five" ], "topic_background": [ "familiar" ], "paper_read": [ "no" ], "search_query": [ "information extraction" ], "question...
{ "caption": [ "Figure 1: A high-level overview of the proposed information extraction approach", "Figure 2: An example illustrating the naive Random Indexing algorithm with unigram atomic units and a (2, 2)-context window as context", "Figure 3: An illustration of supervised contextual classification on ...
Introduction Building knowledge graphs (KG) over Web corpora is an important problem that has galvanized effort from multiple communities over two decades BIBREF0 , BIBREF1 . Automated knowledge graph construction from Web resources involves several different phases. The first phase involves domain discovery, which con...
1808.09409
Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data
This paper studies semantic parsing for interlanguage (L2), taking semantic role labeling (SRL) as a case task and learner Chinese as a case language. We first manually annotate the semantic roles for a set of learner texts to derive a gold standard for automatic SRL. Based on the new data, we then evaluate three off-t...
{ "section_name": [ "Introduction", "An L2-L1 Parallel Corpus", "The Annotation Process", "Inter-annotator Agreement", "Three SRL Systems", "Main Results", "Analysis", "Enhancing SRL with L2-L1 Parallel Data", "The Method", "Experimental Setup", "Conclusion", "Acknowled...
{ "question": [ "What is the baseline model for the agreement-based mode?", "Do the authors suggest why syntactic parsing is so important for semantic role labelling for interlanguages?", "Who manually annotated the semantic roles for the set of learner texts?" ], "question_id": [ "b5d6357d3a9e3d5...
{ "caption": [ "Table 1: Inter-annotator agreement.", "Table 2: Inter-annotator agreement (F-scores) relative to languages and role types.", "Table 3: Performances of the syntax-based and neural syntax-agnostic SRL systems on the L1 and L2 data. “ALL” denotes the overall performance.", "Table 4: Oracl...
Introduction A learner language (interlanguage) is an idiolect developed by a learner of a second or foreign language which may preserve some features of his/her first language. Previously, encouraging results of automatically building the syntactic analysis of learner languages were reported BIBREF0 , but it is still ...
1808.00265
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining
A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image. Current approaches with this capability rely on supervised learning and human annotated groundings to train attention mechanisms inside the VQA architecture. Unfortunately, obtaining human annota...
{ "section_name": [ "Introduction", "Related Work", "VQA Model Structure", "Mining Attention Supervision from Visual Genome", "Implementation Details", "Datasets", "Results", "Conclusions" ], "paragraphs": [ [ "We are interested in the problem of visual question answering...
{ "question": [ "By how much do they outperform existing state-of-the-art VQA models?", "How do they measure the correlation between manual groundings and model generated ones?", "How do they obtain region descriptions and object annotations?" ], "question_id": [ "17f5f4a5d943c91d46552fb75940b67a7...
{ "caption": [ "Figure 1. Interpretable VQA algorithms must ground their answer into image regions that are relevant to the question. In this paper, we aim at providing this ability by leveraging existing region descriptions and object annotations to construct grounding supervision automatically.", "Figure 2....
Introduction We are interested in the problem of visual question answering (VQA), where an algorithm is presented with an image and a question that is formulated in natural language and relates to the contents of the image. The goal of this task is to get the algorithm to correctly answer the question. The VQA task has...
1810.09774
Testing the Generalization Power of Neural Network Models Across NLI Benchmarks
Neural network models have been very successful in natural language inference, with the best models reaching 90% accuracy in some benchmarks. However, the success of these models turns out to be largely benchmark specific. We show that models trained on a natural language inference dataset drawn from one benchmark fail...
{ "section_name": [ "Introduction", "Related Work", "Experimental Setup", "Data", "Model and Training Details", "Experimental Results", "Discussion and Conclusion", "Acknowledgments" ], "paragraphs": [ [ "Natural Language Inference (NLI) has attracted considerable interes...
{ "question": [ "Which training dataset allowed for the best generalization to benchmark sets?", "Which model generalized the best?", "Which models were compared?", "Which datasets were used?" ], "question_id": [ "a48c6d968707bd79469527493a72bfb4ef217007", "b69897deb5fb80bf2adb44f9cbf6280d...
{ "caption": [ "Table 1: Dataset combinations used in the experiments. The rows in bold are baseline experiments, where the test data comes from the same benchmark as the training and development data.", "Table 2: Example sentence pairs from the three datasets.", "Table 3: Model architectures used in the ...
Introduction Natural Language Inference (NLI) has attracted considerable interest in the NLP community and, recently, a large number of neural network-based systems have been proposed to deal with the task. One can attempt a rough categorization of these systems into: a) sentence encoding systems, and b) other neural n...
1910.05608
VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination
Nowadays, Social network sites (SNSs) such as Facebook, Twitter are common places where people show their opinions, sentiments and share information with others. However, some people use SNSs to post abuse and harassment threats in order to prevent other SNSs users from expressing themselves as well as seeking differen...
{ "section_name": [ "Introduction", "System description", "System description ::: System overview", "System description ::: Data pre-processing", "System description ::: Models architecture", "System description ::: Ensemble method", "Experiment", "Conclusion" ], "paragraphs": [ ...
{ "question": [ "What was the baseline?", "Is the data all in Vietnamese?", "What classifier do they use?", "What is private dashboard?", "What is public dashboard?", "What dataset do they use?" ], "question_id": [ "11e376f98df42f487298ec747c32d485c845b5cd", "284ea817fd79bc10b7a82c...
{ "caption": [ "Figure 1. Hate Speech Detection System Overview", "Figure 2. TextCNN model architecture", "Figure 4. LSTM model architecture", "Figure 3. VDCNN model architecture", "Table I F1_MACRO SCORE OF DIFFERENT MODEL", "Figure 5. LSTMCNN model architecture", "Figure 6. SARNN model a...
Introduction Currently, social networks are so popular. Some of the biggest ones include Facebook, Twitter, Youtube,... with extremely number of users. Thus, controlling content of those platforms is essential. For years, social media companies such as Twitter, Facebook, and YouTube have been investing hundreds of mill...
1906.07668
Yoga-Veganism: Correlation Mining of Twitter Health Data
Nowadays social media is a huge platform of data. People usually share their interest, thoughts via discussions, tweets, status. It is not possible to go through all the data manually. We need to mine the data to explore hidden patterns or unknown correlations, find out the dominant topic in data and understand people'...
{ "section_name": [ "Introduction", "Data Collection", "Apache Kafka", "Apache Zookeeper", "Data Extraction using Tweepy", "Data Pre-processing", "Methodology", "Construct document-term matrix", "Topic Modeling", "Optimal number of Topics", "Topic Inference", "Manual An...
{ "question": [ "Do the authors report results only on English data?", "What other interesting correlations are observed?" ], "question_id": [ "8abb96b2450ebccfcc5c98772cec3d86cd0f53e0", "f52ec4d68de91dba66668f0affc198706949ff90" ], "nlp_background": [ "five", "five" ], "topic_back...
{ "caption": [ "Figure 2: Methodology of correlation mining of Twitter health data.", "Figure 3: Topic Modeling using LSA, NMF, and LDA. After topic modeling we identify topic/topics (circles). Red pentagrams and green triangles represent group of co-occurring related words of corresponding topic.", "Figu...
Introduction The main motivation of this work has been started with a question "What do people do to maintain their health?"– some people do balanced diet, some do exercise. Among diet plans some people maintain vegetarian diet/vegan diet, among exercises some people do swimming, cycling or yoga. There are people who d...
1605.04655
Joint Learning of Sentence Embeddings for Relevance and Entailment
We consider the problem of Recognizing Textual Entailment within an Information Retrieval context, where we must simultaneously determine the relevancy as well as degree of entailment for individual pieces of evidence to determine a yes/no answer to a binary natural language question. We compare several variants of neu...
{ "section_name": [ "Introduction", "The Hypothesis Evaluation Task", "Argus Dataset", "AI2-8grade/CK12 Dataset", "MCTest Dataset", "Related Work", "Neural Model", "Sentence Embeddings", "Evidence Integration", "Experimental Setup", "Evaluation", "Analysis", "Conclu...
{ "question": [ "what were the baselines?", "what is the state of the art for ranking mc test answers?", "what is the size of the introduced dataset?", "what datasets did they use?" ], "question_id": [ "225a567eeb2698a9d3f1024a8b270313a6d15f82", "35b10e0dc2cb4a1a31d5692032dc3fbda933bf7d", ...
{ "caption": [ "Figure 51.14 In a pedigree, squares symbolize males, and circles represent females. energy pyramid model is used to show the pattern of traits that are passed from one generation to the next in a family? Energy is passed up a food chain or web from lower to higher trophic levels. Each step of the ...
Introduction Let us consider the goal of building machine reasoning systems based on knowledge from fulltext data like encyclopedic articles, scientific papers or news articles. Such machine reasoning systems, like humans researching a problem, must be able to recover evidence from large amounts of retrieved but mostly...
1911.09483
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
In sequence to sequence learning, the self-attention mechanism proves to be highly effective, and achieves significant improvements in many tasks. However, the self-attention mechanism is not without its own flaws. Although self-attention can model extremely long dependencies, the attention in deep layers tends to over...
{ "section_name": [ "Introduction", "MUSE: Parallel Multi-Scale Attention", "MUSE: Parallel Multi-Scale Attention ::: Attention Mechanism for Global Context Representation", "MUSE: Parallel Multi-Scale Attention ::: Convolution for Local Context Modeling", "MUSE: Parallel Multi-Scale Attention :::...
{ "question": [ "What evaluation metric is used?", "What datasets are used?", "What are three main machine translation tasks?", "How big is improvement in performance over Transformers?" ], "question_id": [ "6e4505609a280acc45b0a821755afb1b3b518ffd", "9bd938859a8b063903314a79f09409af8801c9...
{ "caption": [ "Figure 1: The left figure shows that the performance drops largely with the increase of sentence length on the De-En dataset. The right figure shows the attention map from the 3-th encoder layer. As we can see, the attention map is too dispersed to capture sufficient information. For example, “[EO...
Introduction In recent years, Transformer has been remarkably adept at sequence learning tasks like machine translation BIBREF0, BIBREF1, text classification BIBREF2, BIBREF3, language modeling BIBREF4, BIBREF5, etc. It is solely based on an attention mechanism that captures global dependencies between input tokens, di...
1805.00760
Aspect Term Extraction with History Attention and Selective Transformation
Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment Analysis, aims to extract explicit aspect expressions from online user reviews. We present a new framework for tackling ATE. It can exploit two useful clues, namely opinion summary and aspect detection history. Opinion summary is distilled from the ...
{ "section_name": [ "Introduction", "The ATE Task", "Model Description", "Joint Training", "Datasets", "Comparisons", "Settings", "Main Results", "Ablation Study", "Attention Visualization and Case Study", "Related Work", "Concluding Discussions" ], "paragraphs": [ ...
{ "question": [ "How do they determine the opinion summary?", "Do they explore how useful is the detection history and opinion summary?", "Which dataset(s) do they use to train the model?", "By how much do they outperform state-of-the-art methods?" ], "question_id": [ "282aa4e160abfa7569de7d99...
{ "caption": [ "Figure 1: Framework architecture. The callouts on both sides describe how THA and STN work at each time step. Color printing is preferred.", "Table 1: Statistics of datasets.", "Table 2: Experimental results (F1 score, %). The first four methods are implemented by us, and other results wit...
Introduction Aspect-Based Sentiment Analysis (ABSA) involves detecting opinion targets and locating opinion indicators in sentences in product review texts BIBREF0 . The first sub-task, called Aspect Term Extraction (ATE), is to identify the phrases targeted by opinion indicators in review sentences. For example, in th...
1909.05358
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data. To help satisfy this elementary requirement, we introduce the initial release of the Taskmaster-1 dataset which includes 13,215 task-based dialogs comprising six domains...
{ "section_name": [ "Introduction", "Related work ::: Human-machine vs. human-human dialog", "Related work ::: The Wizard of Oz (WOz) Approach and MultiWOZ", "The Taskmaster Corpus ::: Overview", "The Taskmaster Corpus ::: Two-person, spoken dataset", "The Taskmaster Corpus ::: Two-person, spo...
{ "question": [ "What is the average number of turns per dialog?", "What baseline models are offered?", "Which six domains are covered in the dataset?" ], "question_id": [ "221e9189a9d2431902d8ea833f486a38a76cbd8e", "a276d5931b989e0a33f2a0bc581456cca25658d9", "c21d26130b521c9596a1edd7b9ef3...
{ "caption": [ "Table 1: Statistics comparison: Self-dialogs vs MultiWOZ corpus both containing approximately 10k dialogues each.", "Figure 1: Sample Taskmaster-1 two-person dialog", "Figure 5: Sample one-person, written dialog", "Figure 6: Indicating transaction status with “accept” or “reject”", ...
Introduction Voice-based “personal assistants" such as Apple's SIRI, Microsoft's Cortana, Amazon Alexa, and the Google Assistant have finally entered the mainstream. This development is generally attributed to major breakthroughs in speech recognition and text-to-speech (TTS) technologies aided by recent progress in de...
2003.06279
Using word embeddings to improve the discriminability of co-occurrence text networks
Word co-occurrence networks have been employed to analyze texts both in the practical and theoretical scenarios. Despite the relative success in several applications, traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text. Here we investigate whether...
{ "section_name": [ "Introduction", "Related works", "Material and Methods", "Results and Discussion", "Results and Discussion ::: Performance analysis", "Results and Discussion ::: Effects of considering stopwords and local thresholding", "Conclusion", "Acknowledgments", "Suppleme...
{ "question": [ "What other natural processing tasks authors think could be studied by using word embeddings?", "What is the reason that traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text?", "Do the use word embeddings alone or they...
{ "caption": [ "FIG. 1. Example of a enriched word co-occurrence network created for a text. In this model, after the removal of stopwords, the remaining words are linked whenever they appear in the same context. In the proposed network representation, “virtual” edges are included whenever two nodes (words) are s...
Introduction The ability to construct complex and diverse linguistic structures is one of the main features that set us apart from all other species. Despite its ubiquity, some language aspects remain unknown. Topics such as language origin and evolution have been studied by researchers from diverse disciplines, includ...
2004.03744
e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
The recently proposed SNLI-VE corpus for recognising visual-textual entailment is a large, real-world dataset for fine-grained multimodal reasoning. However, the automatic way in which SNLI-VE has been assembled (via combining parts of two related datasets) gives rise to a large number of errors in the labels of this c...
{ "section_name": [ "Introduction", "SNLI-VE-2.0", "SNLI-VE-2.0 ::: Re-annotation details", "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment", "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Model.", "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Res...
{ "question": [ "Is model explanation output evaluated, what metric was used?", "How many annotators are used to write natural language explanations to SNLI-VE-2.0?", "How many natural language explanations are human-written?", "How much is performance difference of existing model between original and...
{ "caption": [ "Figure 1. Examples from SNLI-VE-2.0. (a) In red, the neutral label from SNLI-VE is wrong, since the picture clearly shows that the crowd is outdoors. We corrected it to entailment in SNLIVE-2.0. (b) In green, an ambiguous instance. There is indeed an American flag in the background but it is very ...
Introduction Inspired by textual entailment BIBREF0, Xie BIBREF1 introduced the visual-textual entailment (VTE) task, which considers semantic entailment between a premise image and a textual hypothesis. Semantic entailment consists in determining if the hypothesis can be concluded from the premise, and assigning to ea...
2001.09332
An Analysis of Word2Vec for the Italian Language
Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In ...
{ "section_name": [ "Introduction", "Word2Vec", "Word2Vec ::: Sampling rate", "Word2Vec ::: Negative sampling", "Implementation details", "Results", "Results ::: Analysis of the various models", "Results ::: Comparison with other models", "Conclusion" ], "paragraphs": [ [ ...
{ "question": [ "What is the dataset used as input to the Word2Vec algorithm?", "Are the word embeddings tested on a NLP task?", "Are the word embeddings evaluated?", "How big is dataset used to train Word2Vec for the Italian Language?", "How does different parameter settings impact the performanc...
{ "caption": [ "Fig. 1. Representation of Word2Vec model.", "Table 1. Accuracy at the 20th epoch for the 6 Skip-gram models analysed when the W dimension of the window and the N value of negative sampling change.", "Fig. 2. Total accuracy using 3COSMUL at different epochs with negative sampling equal to 5...
Introduction In order to make human language comprehensible to a computer, it is obviously essential to provide some word encoding. The simplest approach is the one-hot encoding, where each word is represented by a sparse vector with dimension equal to the vocabulary size. In addition to the storage need, the main prob...
1804.06506
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation
Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional statistical approaches. However, its performance drops considerably in the presence of morphologically rich languages (MRLs). Neural engines usually fail to tackle the large vocabulary and high out-of-vocabulary (OOV) word ...
{ "section_name": [ "Introduction", "NMT for MRLs", "Proposed Architecture", "The Embedded Morphology Table", "The Auxiliary Output Channel", "Combining the Extended Output Layer and the Embedded Morphology Table", "Experimental Study", "Experimental Setting", "Experimental Results...
{ "question": [ "How are the auxiliary signals from the morphology table incorporated in the decoder?", "What type of morphological information is contained in the \"morphology table\"?" ], "question_id": [ "7aab78e90ba1336950a2b0534cc0cb214b96b4fd", "b7fe91e71da8f4dc11e799b3bd408d253230e8c6" ],...
{ "caption": [ "Table 1: Illustrating subword units in MCWs. The boldfaced part indicates the stem.", "Figure 1: The target label that each output channel is supposed to predict when generating the Turkish sequence ‘bu1 terbiyesizlik2 için3’ meaning ‘because3 of3 this1 rudeness2’.", "Figure 2: The archite...
Introduction Morphologically complex words (MCWs) are multi-layer structures which consist of different subunits, each of which carries semantic information and has a specific syntactic role. Table 1 gives a Turkish example to show this type of complexity. This example is a clear indication that word-based models are n...
1904.07342
Learning Twitter User Sentiments on Climate Change with Limited Labeled Data
While it is well-documented that climate change accepters and deniers have become increasingly polarized in the United States over time, there has been no large-scale examination of whether these individuals are prone to changing their opinions as a result of natural external occurrences. On the sub-population of Twitt...
{ "section_name": [ "Background", "Data", "Labeling Methodology", "Outcome Analysis", "Results & Discussion" ], "paragraphs": [ [ "Much prior work has been done at the intersection of climate change and Twitter, such as tracking climate change sentiment over time BIBREF2 , finding co...
{ "question": [ "Do they report results only on English data?", "Do the authors mention any confounds to their study?", "Which machine learning models are used?", "What methodology is used to compensate for limited labelled data?", "Which five natural disasters were examined?" ], "question_id"...
{ "caption": [ "Table 1: Tweets collected for each U.S. 2018 natural disaster", "Figure 1: Four-clustering on sentiment, latitude, and longitude", "Table 2: Selected binary sentiment analysis accuracies", "Figure 2: Pre-event (left) and post-event (right) average climate sentiment aggregated over five...
Background Much prior work has been done at the intersection of climate change and Twitter, such as tracking climate change sentiment over time BIBREF2 , finding correlations between Twitter climate change sentiment and seasonal effects BIBREF3 , and clustering Twitter users based on climate mentalities using network a...
2001.06888
A multimodal deep learning approach for named entity recognition from social media
Named Entity Recognition (NER) from social media posts is a challenging task. User generated content which forms the nature of social media, is noisy and contains grammatical and linguistic errors. This noisy content makes it much harder for tasks such as named entity recognition. However some applications like automat...
{ "section_name": [ "Introduction", "Related Work", "Related Work ::: Unimodal Named Entity Recognition", "Related Work ::: Multimodal Named Entity Recognition", "The Proposed Approach", "Experimental Evaluation", "Experimental Evaluation ::: Dataset", "Experimental Evaluation ::: Expe...
{ "question": [ "Which social media platform is explored?", "What datasets did they use?", "What are the baseline state of the art models?" ], "question_id": [ "0106bd9d54e2f343cc5f30bb09a5dbdd171e964b", "e015d033d4ee1c83fe6f192d3310fb820354a553", "8a871b136ccef78391922377f89491c923a77730"...
{ "caption": [ "Figure 1: A Tweet containing Image and Text: Geoffrey Hinton and Demis Hassabis are referred in text and respective images are provided with Tweet", "Table 1: BIO Tags and their respective meaning", "Figure 2: Proposed CWI Model: Character (left), Word (middle) and Image (right) feature ex...
Introduction A common social media delivery system such as Twitter supports various media types like video, image and text. This media allows users to share their short posts called Tweets. Users are able to share their tweets with other users that are usually following the source user. Hovewer there are rules to prote...
1911.00547
Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization
The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the \#MeToo and \#TimesUp movements. Safecity is an online forum for people who experienced or witnessed sexual harassment to share their personal experiences. It has collected \...
{ "section_name": [ "Introduction", "Related Work", "Data Collection and Annotation", "Proposed Models", "Proposed Models ::: CNN Based Joint Learning Models", "Proposed Models ::: BiLSTM Based Joint Learning Models", "Experiments and Results ::: Experimental Settings", "Experiments an...
{ "question": [ "What is the size of the dataset?", "What model did they use?", "What patterns were discovered from the stories?", "Did they use a crowdsourcing platform?" ], "question_id": [ "acd05f31e25856b9986daa1651843b8dc92c2d99", "8c78b21ec966a5e8405e8b9d3d6e7099e95ea5fb", "af604...
{ "caption": [ "Table 1: Definition of classes in different dimensions about sexual harassment.", "Figure 2: CNN based Joint learning Model. WL and WR are the left and right context around each word.", "Figure 3: BiLSM based Joint Learning Model. Here we use an input of five words as an example.", "Ta...
Introduction Sexual violence, including harassment, is a pervasive, worldwide problem with a long history. This global problem has finally become a mainstream issue thanks to the efforts of survivors and advocates. Statistics show that girls and women are put at high risk of experiencing harassment. Women have about a ...
1604.00117
Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding
The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains. The key to scalability is reducing the amount of training data needed to learn a model for a new task. The proposed multi-task model delivers bette...
{ "section_name": [ "Introduction", "Model", "Data", "Experiments", "Training and Model Configuration Details", "Multi-task Model Experiments", "Open Vocabulary Model Experiments", "Conclusions" ], "paragraphs": [ [ "Slot filling models are a useful method for simple natu...
{ "question": [ "Does the performance increase using their method?", "What tasks are they experimenting with in this paper?", "What is the size of the open vocabulary?" ], "question_id": [ "3c378074111a6cc7319c0db0aced5752c30bfffb", "b464bc48f176a5945e54051e3ffaea9a6ad886d7", "3b40799f25db...
{ "caption": [ "Table 1: Data statistics for each of the four target applications.", "Table 2: Listing of slot types for each app.", "Figure 1: F1 score for multi-task vs. single-task models.", "Table 3: Example labeled sentences from each application.", "Figure 2: OOV rate for each of the n apps....
Introduction Slot filling models are a useful method for simple natural language understanding tasks, where information can be extracted from a sentence and used to perform some structured action. For example, dates, departure cities and destinations represent slots to fill in a flight booking task. This information is...
1908.06725
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
Neural language representation models such as Bidirectional Encoder Representations from Transformers (BERT) pre-trained on large-scale corpora can well capture rich semantics from plain text, and can be fine-tuned to consistently improve the performance on various natural language processing (NLP) tasks. However, the ...
{ "section_name": [ "Introduction", "Language Representation Model", "Commonsense Reasoning", "Distant Supervision", "Commonsense Knowledge Base", "Constructing Pre-training Dataset", "Pre-training BERT_CS", "Experiments", "CommonsenseQA", "Winograd Schema Challenge", "GLUE...
{ "question": [ "How do they select answer candidates for their QA task?" ], "question_id": [ "3c16d4cf5dc23223980d9c0f924cb9e4e6943f13" ], "nlp_background": [ "infinity" ], "topic_background": [ "research" ], "paper_read": [ "no" ], "search_query": [ "commonsense" ], "...
{ "caption": [ "Table 1: Some examples from the CommonsenseQA dataset shown in part A and some related triples from ConceptNet shown in part B. The correct answers in part A are in boldface.", "Table 2: The detailed procedures of constructing one multichoice question answering sample. The ∗ in the fourth step...
Introduction Pre-trained language representation models, including feature-based methods BIBREF0 , BIBREF1 and fine-tuning methods BIBREF2 , BIBREF3 , BIBREF4 , can capture rich language information from text and then benefit many NLP tasks. Bidirectional Encoder Representations from Transformers (BERT) BIBREF4 , as on...
1604.05781
What we write about when we write about causality: Features of causal statements across large-scale social discourse
Identifying and communicating relationships between causes and effects is important for understanding our world, but is affected by language structure, cognitive and emotional biases, and the properties of the communication medium. Despite the increasing importance of social media, much remains unknown about causal sta...
{ "section_name": [ "Introduction", "Dataset, filtering, and corpus selection", "Tagging and corpus comparison", "Cause-trees", "Sentiment analysis", "Topic modeling", "Results", "Discussion", "Acknowledgments" ], "paragraphs": [ [ "Social media and online social netw...
{ "question": [ "How do they extract causality from text?", "What is the source of the \"control\" corpus?", "What are the selection criteria for \"causal statements\"?", "Do they use expert annotations, crowdsourcing, or only automatic methods to analyze the corpora?", "how do they collect the co...
{ "caption": [ "Fig. 1. Measuring the differences between causal and control documents. (A) Examples of processed documents tagged by Parts-of-Speech (POS) or Named Entities (NEs). Unigrams highlighted in red (yellow) are in the bottom 10% (top 10%) of the labMT sentiment scores. (B) Log Odds ratios with 95% Wald...
Introduction Social media and online social networks now provide vast amounts of data on human online discourse and other activities BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . With so much communication taking place online and with social media being capable of hosting powerful misinformation...
1607.06275
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
While question answering (QA) with neural network, i.e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system. To alleviate this problem, we propose a large scale human annotated real-world QA dataset We...
{ "section_name": [ "Introduction", "Factoid QA as Sequence Labeling", "Overview", "Long Short-Term Memory (LSTM)", "Question LSTM", "Evidence LSTMs", "Sequence Labeling", "Training", "WebQA Dataset", "Baselines", "Evaluation Method", "Model Settings", "Comparison w...
{ "question": [ "What languages do they experiment with?", "What are the baselines?", "What was the inter-annotator agreement?", "Did they use a crowdsourcing platform?" ], "question_id": [ "ba48c095c496d01c7717eaa271470c3406bf2d7c", "42a61773aa494f7b12838f71a949034c12084de1", "48c3e61...
{ "caption": [ "Figure 1: Factoid QA as sequence labeling.", "Figure 2: Neural recurrent sequence labeling model for factoid QA. The model consists of three components: “Question LSTM” for computing question representation (rq), “Evidence LSTMs” for analyzing evidence, and “CRF” for producing label sequence w...
Introduction Question answering (QA) with neural network, i.e. neural QA, is an active research direction along the road towards the long-term AI goal of building general dialogue agents BIBREF0 . Unlike conventional methods, neural QA does not rely on feature engineering and is (at least nearly) end-to-end trainable. ...
1603.04553
Unsupervised Ranking Model for Entity Coreference Resolution
Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community. In this paper, we propose a generative, unsupervised ranking model for entity coreference resolution by introducing resolution mode variables. Our un...
{ "section_name": [ "Introduction", "Notations and Definitions", "Generative Ranking Model", "Resolution Mode Variables", "Features", "Model Learning", "Mention Detection", "Experimental Setup", "Results and Comparison", "Conclusion", "Acknowledgements" ], "paragraphs":...
{ "question": [ "Are resolution mode variables hand crafted?", "What are resolution model variables?", "Is the model presented in the paper state of the art?" ], "question_id": [ "80de3baf97a55ea33e0fe0cafa6f6221ba347d0a", "f5707610dc8ae2a3dc23aec63d4afa4b40b7ec1e", "e76139c63da0f861c09746...
{ "caption": [ "Table 1: Feature set for representing a mention under different resolution modes. The Distance feature is for parameter q, while all other features are for parameter t.", "Table 2: Corpora statistics. “ON-Dev” and “ON-Test” are the development and testing sets of the OntoNotes corpus.", "T...
Introduction Entity coreference resolution has become a critical component for many Natural Language Processing (NLP) tasks. Systems requiring deep language understanding, such as information extraction BIBREF2 , semantic event learning BIBREF3 , BIBREF4 , and named entity linking BIBREF5 , BIBREF6 all benefit from ent...
1709.10217
The First Evaluation of Chinese Human-Computer Dialogue Technology
In this paper, we introduce the first evaluation of Chinese human-computer dialogue technology. We detail the evaluation scheme, tasks, metrics and how to collect and annotate the data for training, developing and test. The evaluation includes two tasks, namely user intent classification and online testing of task-orie...
{ "section_name": [ "Introduction", "The First Evaluation of Chinese Human-Computer Dialogue Technology", "Task 1: User Intent Classification", "Task 2: Online Testing of Task-oriented Dialogue", "Evaluation Data", "Evaluation Results", "Conclusion", "Acknowledgements" ], "paragrap...
{ "question": [ "What problems are found with the evaluation scheme?", "How is the data annotated?", "What collection steps do they mention?", "How many intents were classified?", "What was the result of the highest performing system?", "What metrics are used in the evaluation?" ], "questi...
{ "caption": [ "Figure 1: A brief comparison of the open domain chit-chat system and the task-oriented dialogue system.", "Table 1: An example of user intent with category information.", "Table 2: An example of the task-oriented human-computer dialogue.", "Table 3: The statistics of the released data ...
Introduction Recently, human-computer dialogue has been emerged as a hot topic, which has attracted the attention of both academia and industry. In research, the natural language understanding (NLU), dialogue management (DM) and natural language generation (NLG) have been promoted by the technologies of big data and de...
1901.02262
Multi-style Generative Reading Comprehension
This study tackles generative reading comprehension (RC), which consists of answering questions based on textual evidence and natural language generation (NLG). We propose a multi-style abstractive summarization model for question answering, called Masque. The proposed model has two key characteristics. First, unlike m...
{ "section_name": [ "Introduction", "Problem Formulation", "Proposed Model", "Question-Passages Reader", "Passage Ranker", "Answer Possibility Classifier", "Answer Sentence Decoder", "Loss Function", "Setup", "Results", "Conclusion" ], "paragraphs": [ [ "Quest...
{ "question": [ "How do they measure the quality of summaries?", "Does their model also take the expected answer style as input?", "What do they mean by answer styles?", "Is there exactly one \"answer style\" per dataset?", "What are the baselines that Masque is compared against?", "What is th...
{ "caption": [ "Figure 1: Visualization of how our model generates an answer on MS MARCO. Given an answer style (top: NLG, bottom: Q&A), the model controls the mixture of three distributions for generating words from a vocabulary and copying words from the question and multiple passages at each decoding step.", ...
Introduction Question answering has been a long-standing research problem. Recently, reading comprehension (RC), a challenge to answer a question given textual evidence provided in a document set, has received much attention. Here, current mainstream studies have treated RC as a process of extracting an answer span fro...
1908.04917
A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading
Lip reading aims at decoding texts from the movement of a speaker's mouth. In recent years, lip reading methods have made great progress for English, at both word-level and sentence-level. Unlike English, however, Chinese Mandarin is a tone-based language and relies on pitches to distinguish lexical or grammatical mean...
{ "section_name": [ "Introduction", "The Proposed Method", "Pinyin Prediction Sub-network", "Tone Prediction Sub-network", "Character Prediction Sub-network", "CSSMCM Architecture", "Training Strategy", "Dataset", "Implementation Details", "Compared Methods and Evaluation Proto...
{ "question": [ "What was the previous state of the art model for this task?", "What syntactic structure is used to model tones?", "What visual information characterizes tones?" ], "question_id": [ "8a7bd9579d2783bfa81e055a7a6ebc3935da9d20", "27b01883ed947b457d3bab0c66de26c0736e4f90", "971...
{ "caption": [ "Fig. 1. The tone prediction sub-network.", "Table 1. Symbol Definition", "Fig. 2. The character prediction sub-network.", "Fig. 3. The overall of the CSSMCM network. The attention module is omitted for sake of simplicity.", "Table 2. The CMLR dataset. Division of training, validati...
Introduction Lip reading, also known as visual speech recognition, aims to predict the sentence being spoken, given a silent video of a talking face. In noisy environments, where speech recognition is difficult, visual speech recognition offers an alternative way to understand speech. Besides, lip reading has practical...
1906.03338
Dissecting Content and Context in Argumentative Relation Analysis
When assessing relations between argumentative units (e.g., support or attack), computational systems often exploit disclosing indicators or markers that are not part of elementary argumentative units (EAUs) themselves, but are gained from their context (position in paragraph, preceding tokens, etc.). We show that this...
{ "section_name": [ "Introduction", "Related Work", "Argumentative Relation Prediction: Models and Features", "Models", "Feature implementation", "Results", "Discussion", "Conclusion", "Acknowledgments" ], "paragraphs": [ [ "In recent years we have witnessed a great s...
{ "question": [ "Do they report results only on English data?", "How do they demonstrate the robustness of their results?", "What baseline and classification systems are used in experiments?", "How are the EAU text spans annotated?", "How are elementary argumentative units defined?" ], "questi...
{ "caption": [ "Figure 1: A graph representation of a topic (node w/ dashed line), two argumentative premise units (nodes w/ solid line), premise-topic relations (positive or negative) and premise-premise relations (here: attacks).", "Figure 2: Production rule extraction from constituency parse for two differ...
Introduction In recent years we have witnessed a great surge in activity in the area of computational argument analysis (e.g. BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 ), and the emergence of dedicated venues such as the ACL Argument Mining workshop series starting in 2014 BIBREF4 . Argumentative relation classification i...
1602.08741
Gibberish Semantics: How Good is Russian Twitter in Word Semantic Similarity Task?
The most studied and most successful language models were developed and evaluated mainly for English and other close European languages, such as French, German, etc. It is important to study applicability of these models to other languages. The use of vector space models for Russian was recently studied for multiple co...
{ "section_name": [ "Introduction", "Goals of this paper", "Previous work", "Data processing", "Acquiring data", "Corpus preprocessing", "Training the model", "Experimental results", "Properties of the data", "Determining optimal corpus size", "Determining optimal context s...
{ "question": [ "Which Twitter corpus was used to train the word vectors?" ], "question_id": [ "e51d0c2c336f255e342b5f6c3cf2a13231789fed" ], "nlp_background": [ "five" ], "topic_background": [ "unfamiliar" ], "paper_read": [ "no" ], "search_query": [ "twitter" ], "quest...
{ "caption": [ "Table 1. Properties of Twitter corpus (15 full days)", "Table 2. Properties of Twitter corpus (average on daily slices)", "Table 3. Properties of Twitter corpus (different size)", "Table 4. RSpearman for different context size", "Table 5. Comparison with current single-corpus train...
Introduction Word semantic similarity task is an important part of contemporary NLP. It can be applied in many areas, like word sense disambiguation, information retrieval, information extraction and others. It has long history of improvements, starting with simple models, like bag-of-words (often weighted by TF-IDF sc...
1911.12579
A New Corpus for Low-Resourced Sindhi Language with Word Embeddings
Representing words and phrases into dense vectors of real numbers which encode semantic and syntactic properties is a vital constituent in natural language processing (NLP). The success of neural network (NN) models in NLP largely rely on such dense word representations learned on the large unlabeled corpus. Sindhi is ...
{ "section_name": [ "Introduction", "Related work", "Methodology", "Methodology ::: Task description", "Methodology ::: Corpus acquisition", "Methodology ::: Preprocessing", "Methodology ::: Word embedding models", "Methodology ::: GloVe", "Methodology ::: Continuous bag-of-words",...
{ "question": [ "How does proposed word embeddings compare to Sindhi fastText word representations?", "Are trained word embeddings used for any other NLP task?", "How many uniue words are in the dataset?", "How is the data collected, which web resources were used?" ], "question_id": [ "5b6aec1...
{ "caption": [ "Table 1: Comparison of existing and proposed work on Sindhi corpus construction and word embeddings.", "Figure 1: Employed preprocessing pipeline for text cleaning", "Table 2: Complete statistics of collected corpus from multiple resources.", "Figure 2: Frequency distribution of letter...
Introduction Sindhi is a rich morphological, mutltiscript, and multidilectal language. It belongs to the Indo-Aryan language family BIBREF0, with significant cultural and historical background. Presently, it is recognized as is an official language BIBREF1 in Sindh province of Pakistan, also being taught as a compulsor...
1908.10275
The Wiki Music dataset: A tool for computational analysis of popular music
Is it possible use algorithms to find trends in the history of popular music? And is it possible to predict the characteristics of future music genres? In order to answer these questions, we produced a hand-crafted dataset with the intent to put together features about style, psychology, sociology and typology, annotat...
{ "section_name": [ "Motivation, Background and Related Work", "Brief introduction to popular music", "Data Description", "Experiments", "Conclusion Acknowledgments and Future" ], "paragraphs": [ [ "Until recent times, the research in popular music was mostly bound to a non-computati...
{ "question": [ "What trends are found in musical preferences?", "Which decades did they look at?", "How many genres did they collect from?" ], "question_id": [ "75043c17a2cddfce6578c3c0e18d4b7cf2f18933", "95bb3ea4ebc3f2174846e8d422abc076e1407d6a", "3ebdc15480250f130cf8f5ab82b0595e4d870e2f...
{ "caption": [ "Fig. 1. Distribution of genre derivation by super-genres and decade.", "Fig. 2. Distributions of some of the features annotated in the dataset.", "Fig. 3. Trend lines (dashed) of the MUSIC features from 1900.", "TABLE I. RESULTS. *=SCORES CONSIDERED FOR COMPUTING AVG ACCURACY" ], "...
Motivation, Background and Related Work Until recent times, the research in popular music was mostly bound to a non-computational approach BIBREF0 but the availability of new data, models and algorithms helped the rise of new research trends. Computational analysis of music structure BIBREF1 is focused on parsing and a...
2004.02929
An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines
The extraction of anglicisms (lexical borrowings from English) is relevant both for lexicographic purposes and for NLP downstream tasks. We introduce a corpus of European Spanish newspaper headlines annotated with anglicisms and a baseline model for anglicism extraction. In this paper we present: (1) a corpus of 21,570...
{ "section_name": [ "Introduction", "Related Work", "Anglicism: Scope of the Phenomenon", "Corpus description and annotation ::: Corpus description", "Corpus description and annotation ::: Corpus description ::: Main Corpus", "Corpus description and annotation ::: Corpus description ::: Supple...
{ "question": [ "Does the paper mention other works proposing methods to detect anglicisms in Spanish?", "What is the performance of the CRF model on the task described?", "Does the paper motivate the use of CRF as the baseline model?", "What are the handcrafted features used?" ], "question_id": [...
{ "caption": [ "Table 1: Number of headlines, tokens and anglicisms per corpus subset.", "Table 2: Percentage of headlines with anglicisms per section.", "Figure 1: Decision steps to follow during the annotation process to decide whether to annotate a word as a borrowing.", "Table 3: Types of embeddin...
Introduction The study of English influence in the Spanish language has been a hot topic in Hispanic linguistics for decades, particularly concerning lexical borrowing or anglicisms BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6. Lexical borrowing is a phenomenon that affects all languages and constitut...
1908.06809
Style Transfer for Texts: to Err is Human, but Error Margins Matter
This paper shows that standard assessment methodology for style transfer has several significant problems. First, the standard metrics for style accuracy and semantics preservation vary significantly on different re-runs. Therefore one has to report error margins for the obtained results. Second, starting with certain ...
{ "section_name": [ "Introduction", "Related Work", "Style transfer", "Experiments", "Experiments ::: Error margins matter", "Experiments ::: Delete, duplicate and conquer", "Conclusion", "Supplemental Material" ], "paragraphs": [ [ "Deep generative models attract a lot o...
{ "question": [ "What is state of the art method?", "By how much do proposed architectures autperform state-of-the-art?", "What are three new proposed architectures?", "How much does the standard metrics for style accuracy vary on different re-runs?" ], "question_id": [ "41830ebb8369a24d490e50...
{ "caption": [ "Figure 1: Test results of multiple runs for four different architectures retrained several times from scratch. Indepth description of the architectures can be found in Section 3.", "Figure 2: Overview of the self-reported results for sentiment transfer on Yelp! reviews. Results of (Romanov et ...
Introduction Deep generative models attract a lot of attention in recent years BIBREF0. Such methods as variational autoencoders BIBREF1 or generative adversarial networks BIBREF2 are successfully applied to a variety of machine vision problems including image generation BIBREF3, learning interpretable image representa...
1707.00110
Efficient Attention using a Fixed-Size Memory Representation
The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is m...
{ "section_name": [ "Introduction", "Sequence-to-Sequence Model with Attention", "Memory-Based Attention Model", "Model Interpretations", "Position Encodings (PE)", "Toy Copying Experiment", "Machine Translation", "Visualizing Attention", "Related Work", "Conclusion" ], "pa...
{ "question": [ "Which baseline methods are used?", "How much is the BLEU score?", "Which datasets are used in experiments?" ], "question_id": [ "2d3bf170c1647c5a95abae50ee3ef3b404230ce4", "6e8c587b6562fafb43a7823637b84cd01487059a", "ab9453fa2b927c97b60b06aeda944ac5c1bfef1e" ], "nlp_ba...
{ "caption": [ "Figure 1: Memory Attention model architecture. K attention vectors are predicted during encoding, and a linear combination is chosen during decoding. In our example,K=3.", "Figure 2: Surface for the position encodings.", "Table 1: BLEU scores and computation times with varyingK and sequenc...
Introduction Sequence-to-sequence models BIBREF0 , BIBREF1 have achieved state of the art results across a wide variety of tasks, including Neural Machine Translation (NMT) BIBREF2 , BIBREF3 , text summarization BIBREF4 , BIBREF5 , speech recognition BIBREF6 , BIBREF7 , image captioning BIBREF8 , and conversational mod...