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Content Differences in Syntactic and Semantic RepresentationsMar 15 2019Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which ... More
A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional NetworksMar 15 2019With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a time-consuming process. A technique that could add a reference citation at the appropriate place in a sentence ... More
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse TasksMar 14 2019While most previous work has focused on different pretraining objectives and architectures for transfer learning, we ask how to best adapt the pretrained model to a given target task. We focus on the two most common forms of adaptation, feature extraction ... More
Generative adversarial network-based glottal waveform model for statistical parametric speech synthesisMar 14 2019Recent studies have shown that text-to-speech synthesis quality can be improved by using glottal vocoding. This refers to vocoders that parameterize speech into two parts, the glottal excitation and vocal tract, that occur in the human speech production ... More
OffensEval at SemEval-2018 Task 6: Identifying and Categorizing Offensive Language in Social MediaMar 14 2019This document describes our approach to building an Offensive Language Classifier. More specifically, the coursework required us to build three classifiers with slightly different goals: - Offensive language identification: would classify a tweet as offensive ... More
Interactive Concept Mining on Personal Data -- Bootstrapping Semantic ServicesMar 14 2019Semantic services (e.g. Semantic Desktops) are still afflicted by a cold start problem: in the beginning, the user's personal information sphere, i.e. files, mails, bookmarks, etc., is not represented by the system. Information extraction tools used to ... More
MirrorGAN: Learning Text-to-image Generation by RedescriptionMar 14 2019Generating an image from a given text description has two goals: visual realism and semantic consistency. Although significant progress has been made in generating high-quality and visually realistic images using generative adversarial networks, guaranteeing ... More
A Deep Patent Landscaping Model using Transformer and Graph Convolutional NetworkMar 14 2019Patent landscaping is a method that is employed for searching related patents during the process of a research and development (R&D) project. To avoid the risk of patent infringement and to follow the current trends of technology development, patent landscaping ... More
Survey of Text-based Epidemic Intelligence: A Computational Linguistic PerspectiveMar 14 2019Epidemic intelligence deals with the detection of disease outbreaks using formal (such as hospital records) and informal sources (such as user-generated text on the web) of information. In this survey, we discuss approaches for epidemic intelligence that ... More
Complexity-entropy analysis at different levels of organization in written languageMar 14 2019Written language is complex. A written text can be considered an attempt to convey a meaningful message which ends up being constrained by language rules, context dependence and highly redundant in its use of resources. Despite all these constraints, ... More
Consistent Dialogue Generation with Self-supervised Feature LearningMar 13 2019Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. In this paper, we propose a neural conversation model that generates consistent responses by maintaining certain ... More
Low-Resource Syntactic Transfer with Unsupervised Source ReorderingMar 13 2019We describe a cross-lingual transfer method for dependency parsing that takes into account the problem of word order differences between source and target languages. Our model only relies on the Bible, a considerably smaller parallel data than the commonly ... More
GASC: Genre-Aware Semantic Change for Ancient GreekMar 13 2019Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a critical challenge in diachronic research, and is relevant to a range of NLP tasks, including information ... More
Benchmarking Natural Language Understanding Services for building Conversational AgentsMar 13 2019We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogue Act (DA) or Intent specifications, while making this process accessible ... More
Adversarial attacks against Fact Extraction and VERificationMar 13 2019This paper describes a baseline for the second iteration of the Fact Extraction and VERification shared task (FEVER2.0) which explores the resilience of systems through adversarial evaluation. We present a collection of simple adversarial attacks against ... More
SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature IndicatorsMar 13 2019This paper describes, develops, and validates SciLens, a method to evaluate the quality of scientific news articles. The starting point for our work are structured methodologies that define a series of quality aspects for manually evaluating news. Based ... More
Overview of the Ugglan Entity Discovery and Linking SystemMar 13 2019Ugglan is a system designed to discover named entities and link them to unique identifiers in a knowledge base. It is based on a combination of a name and nominal dictionary derived from Wikipedia and Wikidata, a named entity recognition module (NER) ... More
MMKG: Multi-Modal Knowledge GraphsMar 13 2019We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities ... More
Market Trend Prediction using Sentiment Analysis: Lessons Learned and Paths ForwardMar 13 2019Financial market forecasting is one of the most attractive practical applications of sentiment analysis. In this paper, we investigate the potential of using sentiment \emph{attitudes} (positive vs negative) and also sentiment \emph{emotions} (joy, sadness, ... More
Sub-event detection from Twitter streams as a sequence labeling problemMar 13 2019This paper introduces improved methods for sub-event detection in social media streams, by applying neural sequence models not only on the level of individual posts, but also directly on the stream level. Current approaches to identify sub-events within ... More
End-To-End Speech Recognition Using A High Rank LSTM-CTC Based ModelMar 12 2019Long Short Term Memory Connectionist Temporal Classification (LSTM-CTC) based end-to-end models are widely used in speech recognition due to its simplicity in training and efficiency in decoding. In conventional LSTM-CTC based models, a bottleneck projection ... More
Syntax-aware Neural Semantic Role Labeling with SupertagsMar 12 2019We introduce a new syntax-aware model for dependency-based semantic role labeling that outperforms syntax-agnostic models for English and Spanish. We use a BiLSTM to tag the text with supertags extracted from dependency parses, and we feed these supertags, ... More
Bootstrapping Method for Developing Part-of-Speech Tagged Corpus in Low Resource Languages Tagset - A Focus on an African IgboMar 12 2019Most languages, especially in Africa, have fewer or no established part-of-speech (POS) tagged corpus. However, POS tagged corpus is essential for natural language processing (NLP) to support advanced researches such as machine translation, speech recognition, ... More
"Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic ResponsesMar 12 2019In the past few years, social media has risen as a platform where people express and share personal incidences about abuse, violence and mental health issues. There is a need to pinpoint such posts and learn the kind of response expected. For this purpose, ... More
Topological Analysis of Syntactic StructuresMar 12 2019We use the persistent homology method of topological data analysis and dimensional analysis techniques to study data of syntactic structures of world languages. We analyze relations between syntactic parameters in terms of dimensionality, of hierarchical ... More
On the Pitfalls of Measuring Emergent CommunicationMar 12 2019How do we know if communication is emerging in a multi-agent system? The vast majority of recent papers on emergent communication show that adding a communication channel leads to an increase in reward or task success. This is a useful indicator, but ... More
Scaling Multi-Domain Dialogue State Tracking via Query ReformulationMar 12 2019We present a novel approach to dialogue state tracking and referring expression resolution tasks. Successful contextual understanding of multi-turn spoken dialogues requires resolving referring expressions across turns and tracking the entities relevant ... More
Few-Shot and Zero-Shot Learning for Historical Text NormalizationMar 12 2019Historical text normalization often relies on small training datasets. Recent work has shown that multi-task learning can sometimes lead to significant improvements by exploiting synergies with related datasets, but there has been no systematic study ... More
Syllable-based Neural Named Entity Recognition for Myanmar LanguageMar 12 2019Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar natural language processing research work. In this work, NER for Myanmar language is treated as a sequence tagging problem and the effectiveness of deep neural networks on NER ... More
Context-Aware Learning for Neural Machine TranslationMar 12 2019Interest in larger-context neural machine translation, including document-level and multi-modal translation, has been growing. Multiple works have proposed new network architectures or evaluation schemes, but potentially helpful context is still sometimes ... More
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic ModelingMar 11 2019Mar 13 2019Monaural speech enhancement has made dramatic advances since the introduction of deep learning a few years ago. Although enhanced speech has been demonstrated to have better intelligibility and quality for human listeners, feeding it directly to automatic ... More
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text ClassificationMar 11 2019Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in society at large. In this paper, we introduce a suite of threshold-agnostic ... More
Practical Semantic Parsing for Spoken Language UnderstandingMar 11 2019Mar 13 2019Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework ... More
ETNLP: A Toolkit for Extraction, Evaluation and Visualization of Pre-trained Word EmbeddingsMar 11 2019In this paper, we introduce a comprehensive toolkit, ETNLP, which can evaluate, extract, and visualize multiple sets of pre-trained word embeddings. First, for evaluation, ETNLP analyses the quality of pre-trained embeddings based on an input word analogy ... More
Scaling in Words on TwitterMar 11 2019Scaling properties of language are a useful tool for understanding generative processes in texts. We investigate the scaling relations in citywise Twitter corpora coming from the Metropolitan and Micropolitan Statistical Areas of the United States. We ... More
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria LearningMar 11 2019The ambiguous annotation criteria bring into the divergence of Chinese Word Segmentation (CWS) datasets with various granularities. Multi-criteria learning leverage the annotation style of individual datasets and mine their common basic knowledge. In ... More
Partially Shuffling the Training Data to Improve Language ModelsMar 11 2019Although SGD requires shuffling the training data between epochs, currently none of the word-level language modeling systems do this. Naively shuffling all sentences in the training data would not permit the model to learn inter-sentence dependencies. ... More
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree ParsingMar 11 2019This paper describes a simple UCCA semantic graph parsing approach. The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote and discontinuous links for future recovery. In ... More
An Innovative Word Encoding Method For Text Classification Using Convolutional Neural NetworkMar 11 2019Text classification plays a vital role today especially with the intensive use of social networking media. Recently, different architectures of convolutional neural networks have been used for text classification in which one-hot vector, and word embedding ... More
Redditors in Recovery: Text Mining Reddit to Investigate Transitions into Drug AddictionMar 11 2019Increasing rates of opioid drug abuse and heightened prevalence of online support communities underscore the necessity of employing data mining techniques to better understand drug addiction using these rapidly developing online resources. In this work, ... More
Contextualised concept embedding for efficiently adapting natural language processing models for phenotype identificationMar 10 2019Many efforts have been put to use automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to picture comprehensive patient profiles for delivering better health-care. Reusing NLP models in ... More
Contextualised concept embedding for efficiently adapting natural language processing models for phenotype identificationMar 10 2019Mar 13 2019Many efforts have been put to use automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to picture comprehensive patient profiles for delivering better health-care. Reusing NLP models in ... More
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning ApproachesMar 10 2019This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data. In particular, we look into the application of (i) rule-based, (ii) deep learning and (iii) transfer learning systems for the ... More
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove ThemMar 09 2019Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing ... More
Logic Rules Powered Knowledge Graph EmbeddingMar 09 2019Large scale knowledge graph embedding has attracted much attention from both academia and industry in the field of Artificial Intelligence. However, most existing methods concentrate solely on fact triples contained in the given knowledge graph. Inspired ... More
Mutual Clustering on Comparative Texts via Heterogeneous Information NetworksMar 09 2019Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be ``comparative'' since they may be semantically correlated and thus provide us with different perspectives ... More
Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom MonitoringMar 08 2019Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data is even scarcer in healthcare. In this work, we investigate fast prototyping of a dialogue comprehension system ... More
Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context InjectionMar 08 2019When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must "guess" this missing information, often leading to incorrect translations in the given context. ... More
Attribute Acquisition in Ontology based on Representation Learning of Hierarchical Classes and AttributesMar 08 2019Attribute acquisition for classes is a key step in ontology construction, which is often achieved by community members manually. This paper investigates an attention-based automatic paradigm called TransATT for attribute acquisition, by learning the representation ... More
CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual DialogMar 07 2019Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image, using the conversation history as context. It entails challenges in vision, language, reasoning, and grounding. However, studying these subtasks in isolation ... More
Learning to Speak and Act in a Fantasy Text Adventure GameMar 07 2019We introduce a large scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within ... More
Option Comparison Network for Multiple-choice Reading ComprehensionMar 07 2019Multiple-choice reading comprehension (MCRC) is the task of selecting the correct answer from multiple options given a question and an article. Existing MCRC models typically either read each option independently or compute a fixed-length representation ... More
Integrating Artificial and Human Intelligence for Efficient TranslationMar 07 2019Current advances in machine translation increase the need for translators to switch from traditional translation to post-editing of machine-translated text, a process that saves time and improves quality. Human and artificial intelligence need to be integrated ... More
Neural Language Modeling with Visual FeaturesMar 07 2019Multimodal language models attempt to incorporate non-linguistic features for the language modeling task. In this work, we extend a standard recurrent neural network (RNN) language model with features derived from videos. We train our models on data that ... More
Small-world networks for summarization of biomedical articlesMar 07 2019In recent years, many methods have been developed to identify important portions of text documents. Summarization tools can utilize these methods to extract summaries from large volumes of textual information. However, to identify concepts representing ... More
Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language ModelsMar 07 2019The goal of this paper is to simulate the benefits of jointly applying active learning (AL) and semi-supervised training (SST) in a new speech recognition application. Our data selection approach relies on confidence filtering, and its impact on both ... More
Predicting Research Trends From ArxivMar 07 2019We perform trend detection on two datasets of Arxiv papers, derived from its machine learning (cs.LG) and natural language processing (cs.CL) categories. Our approach is bottom-up: we first rank papers by their normalized citation counts, then group top-ranked ... More
Arabic natural language processing: An overviewMar 07 2019Arabic is recognised as the 4th most used language of the Internet. Arabic has three main varieties: (1) classical Arabic (CA), (2) Modern Standard Arabic (MSA), (3) Arabic Dialect (AD). MSA and AD could be written either in Arabic or in Roman script ... More
Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities DomainMar 07 2019Word embeddings are already well studied in the general domain, usually trained on large text corpora, and have been evaluated for example on word similarity and analogy tasks, but also as an input to downstream NLP processes. In contrast, in this work ... More
Multi-Instance Learning for End-to-End Knowledge Base Question AnsweringMar 06 2019End-to-end training has been a popular approach for knowledge base question answering (KBQA). However, real world applications often contain answers of varied quality for users' questions. It is not appropriate to treat all available answers of a user ... More
A Character-Level Approach to the Text Normalization Problem Based on a New Causal EncoderMar 06 2019Text normalization is a ubiquitous process that appears as the first step of many Natural Language Processing problems. However, previous Deep Learning approaches have suffered from so-called silly errors, which are undetectable on unsupervised frameworks, ... More
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity TypingMar 06 2019Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such techniques, however, are not directly applicable to more open and practical ... More
Sentence Embedding Alignment for Lifelong Relation ExtractionMar 06 2019Conventional approaches to relation extraction usually require a fixed set of pre-defined relations. Such requirement is hard to meet in many real applications, especially when new data and relations are emerging incessantly and it is computationally ... More
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language NavigationMar 06 2019We present FAST NAVIGATOR, a general framework for action decoding, which yields state-of-the-art results on the recent Room-to-Room (R2R) Vision-and-Language navigation challenge of Anderson et. al. (2018). Given a natural language instruction and photo-realistic ... More
SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCAMar 06 2019We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological ... More
KBQA: Learning Question Answering over QA Corpora and Knowledge BasesMar 06 2019Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and concise results, provided that natural language questions can be understood and mapped ... More
Dixit: Interactive Visual Storytelling via Term ManipulationMar 06 2019Mar 11 2019In this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence. The user initiates the process by uploading a sequence of photos. Dixit first extracts ... More
Dixit: Interactive Visual Storytelling via Term ManipulationMar 06 2019In this paper, we introduceDixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence. The user initiates the process by up-loading a sequence of photos. Dixit first extracts ... More
Bidirectional Attentive Memory Networks for Question Answering over Knowledge BasesMar 06 2019When answering natural language questions over knowledge bases (KB), different question components and KB aspects play different roles. However, most existing embedding-based methods for knowledge base question answering (KBQA) ignore the subtle inter-relationships ... More
AAAI-2019 Workshop on Games and Simulations for Artificial IntelligenceMar 06 2019This volume represents the accepted submissions from the AAAI-2019 Workshop on Games and Simulations for Artificial Intelligence held on January 29, 2019 in Honolulu, Hawaii, USA.
SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational ClassificationMar 06 2019We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem. Reducing the distance between ... More
Persona-Aware Tips GenerationMar 06 2019Tips, as a compacted and concise form of reviews, were paid less attention by researchers. In this paper, we investigate the task of tips generation by considering the `persona' information which captures the intrinsic language style of the users or the ... More
Negative Training for Neural Dialogue Response GenerationMar 06 2019Although deep learning models have brought tremendous advancements to the field of open-domain dialogue response generation, recent research results have revealed that the trained models have undesirable generation behaviors, such as malicious responses ... More
Language and Dialect Identification of Cuneiform TextsMar 05 2019This article introduces a corpus of cuneiform texts from which the dataset for the use of the Cuneiform Language Identification (CLI) 2019 shared task was derived as well as some preliminary language identification experiments conducted using that corpus. ... More
Exploiting Emotions for Fake News Detection on Social MediaMar 05 2019Microblog has become a popular platform for people to post, share, and seek information due to its convenience and low cost. However, it also facilitates the generation and propagation of fake news, which could cause detrimental societal consequences. ... More
Improving Cross-Domain Chinese Word Segmentation with Word EmbeddingsMar 05 2019Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose ... More
Improving Cross-Domain Chinese Word Segmentation with Word EmbeddingsMar 05 2019Mar 11 2019Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose ... More
Polylingual WordnetMar 04 2019Princeton WordNet is one of the most important resources for natural language processing, but is only available for English. While it has been translated using the expand approach to many other languages, this is an expensive manual process. Therefore ... More
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution NetworksMar 04 2019We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, ... More
Traditional Machine Learning for Pitch DetectionMar 04 2019Pitch detection is a fundamental problem in speech processing as F0 is used in a large number of applications. Recent articles have proposed deep learning for robust pitch tracking. In this paper, we consider voicing detection as a classification problem ... More
Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word EmbeddingsMar 04 2019In this research, we manually create high-quality datasets in the digital humanities domain for the evaluation of language models, specifically word embedding models. The first step comprises the creation of unigram and n-gram datasets for two fantasy ... More
From Knowledge Map to Mind Map: Artificial ImaginationMar 04 2019Mar 06 2019Imagination is one of the most important factors which makes an artistic painting unique and impressive. With the rapid development of Artificial Intelligence, more and more researchers try to create painting with AI technology automatically. However, ... More
From Knowledge Map to Mind Map: Artificial ImaginationMar 04 2019Imagination is one of the most important factors which makes an artistic painting unique and impressive. With the rapid development of Artificial Intelligence, more and more researchers try to create painting with AI technology automatically. However, ... More
SECNLP: A Survey of Embeddings in Clinical Natural Language ProcessingMar 04 2019Mar 05 2019Traditional representations like Bag of words are high dimensional, sparse and ignore the order as well as syntactic and semantic information. Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well ... More
SECNLP: A Survey of Embeddings in Clinical Natural Language ProcessingMar 04 2019Traditional representations like Bag of words are high dimensional, sparse and ignore the order as well as syntactic and semantic information. Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well ... More
Structural Supervision Improves Learning of Non-Local Grammatical DependenciesMar 03 2019State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail, and have been shown to acquire a number of non-local grammatical dependencies with some success. Here we investigate whether supervision ... More
Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpusMar 03 2019Machine learning has shown promise for automatic detection of Alzheimer's disease (AD) through speech; however, efforts are hampered by a scarcity of data, especially in languages other than English. We propose a method to learn a correspondence between ... More
Improving Referring Expression Grounding with Cross-modal Attention-guided ErasingMar 03 2019Referring expression grounding aims at locating certain objects or persons in an image with a referring expression, where the key challenge is to comprehend and align various types of information from visual and textual domain, such as visual attributes, ... More
Calibration of Encoder Decoder Models for Neural Machine TranslationMar 03 2019We study the calibration of several state of the art neural machine translation(NMT) systems built on attention-based encoder-decoder models. For structured outputs like in NMT, calibration is important not just for reliable confidence with predictions, ... More
Predicting and interpreting embeddings for out of vocabulary words in downstream tasksMar 02 2019We propose a novel way to handle out of vocabulary (OOV) words in downstream natural language processing (NLP) tasks. We implement a network that predicts useful embeddings for OOV words based on their morphology and on the context in which they appear. ... More
Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corporaMar 01 2019The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information and actionable insights from the latest research is becoming a considerable ... More
Learning To Follow Directions in Street ViewMar 01 2019Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision. We propose an instruction-following task that ... More
Data-driven Approach for Quality Evaluation on Knowledge Sharing PlatformMar 01 2019In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge ... More
A Framework for Detecting Event related Sentiments of a CommunityMar 01 2019Social media has revolutionized human communication and styles of interaction. Due to its easiness and effective medium, people share and exchange information, carry out discussion on various events, and express their opinions. For effective policy making ... More
KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube VideosMar 01 2019In this paper, we describe KT-Speech-Crawler: an approach for automatic dataset construction for speech recognition by crawling YouTube videos. We outline several filtering and post-processing steps, which extract samples that can be used for training ... More
Open Information Extraction from Question-Answer PairsMar 01 2019Open Information Extraction (OpenIE) extracts meaningful structured tuples from free-form text. Most previous work on OpenIE considers extracting data from one sentence at a time. We describe NeurON, a system for extracting tuples from question-answer ... More
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over ParagraphsMar 01 2019Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of these systems, showing that there is much work left to be ... More
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled DataMar 01 2019Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to ... More
Improving Grounded Natural Language Understanding through Human-Robot DialogMar 01 2019Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use ... More
Non-Parametric Adaptation for Neural Machine TranslationFeb 28 2019Neural Networks trained with gradient descent are known to be susceptible to catastrophic forgetting caused by parameter shift during the training process. In the context of Neural Machine Translation (NMT) this results in poor performance on heterogeneous ... More