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The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic StructuresMay 16 2019Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis ... More
Dynamically Fused Graph Network for Multi-hop ReasoningMay 16 2019Text-based question answering (TBQA) has been studied extensively in recent years. Most existing approaches focus on finding the answer to a question within a single paragraph. However, many difficult questions require multiple supporting evidence from ... More
Gated Convolutional Neural Networks for Domain AdaptationMay 16 2019Domain Adaptation explores the idea of how to maximize performance on a target domain, distinct from source domain, upon which the classifier was trained. This idea has been explored for the task of sentiment analysis extensively. The training of reviews ... More
TraceWalk: Semantic-based Process Graph Embedding for Consistency CheckingMay 16 2019Process consistency checking (PCC), an interdiscipline of natural language processing (NLP) and business process management (BPM), aims to quantify the degree of (in)consistencies between graphical and textual descriptions of a process. However, previous ... More
Tracing cultural diachronic semantic shifts in Russian using word embeddings: test sets and baselinesMay 16 2019The paper introduces manually annotated test sets for the task of tracing diachronic (temporal) semantic shifts in Russian. The two test sets are complementary in that the first one covers comparatively strong semantic changes occurring to nouns and adjectives ... More
Effective Sentence Scoring Method using Bidirectional Language Model for Speech RecognitionMay 16 2019In automatic speech recognition, many studies have shown performance improvements using language models (LMs). Recent studies have tried to use bidirectional LMs (biLMs) instead of conventional unidirectional LMs (uniLMs) for rescoring the $N$-best list ... More
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party DialogueMay 16 2019Humans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations have been proposed. Such entity-centric models have shown empirical success, ... More
Using Entity Relations for Opinion Mining of Vietnamese CommentsMay 16 2019In this paper, we propose several novel techniques to extract and mining opinions of Vietnamese reviews of customers about a number of products traded on e-commerce in Vietnam. The assessment is based on the emotional level of customers on a specific ... More
Machine Learning based English Sentiment AnalysisMay 16 2019Sentiment analysis or opinion mining aims to determine attitudes, judgments and opinions of customers for a product or a service. This is a great system to help manufacturers or servicers know the satisfaction level of customers about their products or ... More
Latent Universal Task-Specific BERTMay 16 2019This paper describes a language representation model which combines the Bidirectional Encoder Representations from Transformers (BERT) learning mechanism described in Devlin et al. (2018) with a generalization of the Universal Transformer model described ... More
A Simple Dual-decoder Model for Generating Response with SentimentMay 16 2019How to generate human like response is one of the most challenging tasks for artificial intelligence. In a real application, after reading the same post different people might write responses with positive or negative sentiment according to their own ... More
Joint Source-Target Self Attention with Locality ConstraintsMay 16 2019The dominant neural machine translation models are based on the encoder-decoder structure, and many of them rely on an unconstrained receptive field over source and target sequences. In this paper we study a new architecture that breaks with both conventions. ... More
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document SummarizationMay 16 2019Neural 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 ... More
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speechMay 16 2019The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies. One prominent clinical application is a computer-assisted speech training system ... More
Incorporating Sememes into Chinese Definition ModelingMay 16 2019Chinese 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 ... More
Controlled CNN-based Sequence Labeling for Aspect ExtractionMay 15 2019One key task of fine-grained sentiment analysis on reviews is to extract aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using a modified CNN called controlled CNN (Ctrl). The modified CNN ... More
Exact Hard Monotonic Attention for Character-Level TransductionMay 15 2019Many common character-level, string-to-string transduction tasks, e.g., grapheme-to-phoneme conversion and morphological inflection, consist almost exclusively of monotonic transduction. Neural sequence-to-sequence models with soft attention, non-monotonic ... More
What do you learn from context? Probing for sentence structure in contextualized word representationsMay 15 2019Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce ... More
A Surprisingly Robust Trick for Winograd Schema ChallengeMay 15 2019The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this paper, we show that the performance of three language models on WSC273 strongly ... More
Representing Schema Structure with Graph Neural Networks for Text-to-SQL ParsingMay 15 2019Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In \spider{}, a recently-released text-to-SQL dataset, ... More
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching DatasetsMay 15 2019Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process. However, biased datasets can also hurt the generalization performance of trained ... More
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching DatasetsMay 15 2019May 16 2019Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process. However, biased datasets can also hurt the generalization performance of trained ... More
Dual Supervised Learning for Natural Language Understanding and GenerationMay 15 2019May 16 2019Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP field. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation ... More
Dual Supervised Learning for Natural Language Understanding and GenerationMay 15 2019Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP field. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation ... More
Towards Interlingua Neural Machine TranslationMay 15 2019A common intermediate language representation or an interlingua is the holy grail in machine translation. Thanks to the new neural machine translation approach, it seems that there are good perspectives towards this goal. In this paper, we propose a new ... More
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical FeaturesMay 15 2019Neural networks (NN) are considered as black-boxes due to the lack of explainability and transparency of their decisions. This significantly hampers their deployment in environments where explainability is essential along with the accuracy of the system. ... More
Aligning Visual Regions and Textual Concepts: Learning Fine-Grained Image Representations for Image CaptioningMay 15 2019In image-grounded text generation, fine-grained representations of the image are considered to be of paramount importance. Most of the current systems incorporate visual features and textual concepts as a sketch of an image. However, plainly inferred ... More
Towards Comparing Programming ParadigmsMay 15 2019Rapid technological progress in computer sciences finds solutions and at the same time creates ever more complex requirements. Due to an evolving complexity todays programming languages provide powerful frameworks which offer standard solutions for recurring ... More
When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical CohesionMay 15 2019Though machine translation errors caused by the lack of context beyond one sentence have long been acknowledged, the development of context-aware NMT systems is hampered by several problems. Firstly, standard metrics are not sensitive to improvements ... More
Demographic Inference and Representative Population Estimates from Multilingual Social Media DataMay 15 2019Social media provide access to behavioural data at an unprecedented scale and granularity. However, using these data to understand phenomena in a broader population is difficult due to their non-representativeness and the bias of statistical inference ... More
BERT Rediscovers the Classical NLP PipelineMay 15 2019Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the ... More
Passage Ranking with Weak SupervsionMay 15 2019In this paper, we propose a \textit{weak supervision} framework for neural ranking tasks based on the data programming paradigm \citep{Ratner2016}, which enables us to leverage multiple weak supervision signals from different sources. Empirically, we ... More
Generative Design in Minecraft: Chronicle ChallengeMay 14 2019We introduce the Chronicle Challenge as an optional addition to the Settlement Generation Challenge in Minecraft. One of the foci of the overall competition is adaptive procedural content generation (PCG), an arguably under-explored problem in computational ... More
Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology DatasetMay 14 2019Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach based on deep learning to automatically identify clinically important recommendations ... More
Ontology-Aware Clinical Abstractive SummarizationMay 14 2019Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological ... More
Curriculum Learning for Domain Adaptation in Neural Machine TranslationMay 14 2019We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular ... More
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment AnalysisMay 14 2019Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs ... More
Misleading Failures of Partial-input BaselinesMay 14 2019Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines (e.g., hypothesis-only or image-only models). While the success of a partial-input baseline indicates a dataset is cheatable, our work cautions the ... More
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question AnsweringMay 14 2019This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the architecture of the machine reading model, only requiring access to the ... More
Sparse Sequence-to-Sequence ModelsMay 14 2019Sequence-to-sequence models are a powerful workhorse of NLP. Most variants employ a softmax transformation in both their attention mechanism and output layer, leading to dense alignments and strictly positive output probabilities. This density is wasteful, ... More
A Unified Linear-Time Framework for Sentence-Level Discourse ParsingMay 14 2019We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) in a text, and a discourse ... More
Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense DisambiguationMay 14 2019In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress ... More
Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue SystemsMay 14 2019Natural language generation (NLG) is an essential component of task-oriented dialogue systems. Despite the recent success of neural approaches for NLG, they are typically developed for particular domains with rich annotated training examples. In this ... More
Generic Encodings of Constructor Rewriting SystemsMay 14 2019Rewriting is a formalism widely used in computer science and mathematical logic. The classical formalism has been extended, in the context of functional languages, with an order over the rules and, in the context of rewrite based languages, with the negation ... More
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent RepresentationMay 14 2019Disentangling the content and style in the latent space is prevalent in unpaired text style transfer. However, two major issues exist in most of the current neural models. 1) It is difficult to completely strip the style information from the semantics ... More
How to Fine-Tune BERT for Text Classification?May 14 2019Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in ... More
A Dynamic Evolutionary Framework for Timeline Generation based on Distributed RepresentationsMay 14 2019May 15 2019Given the collection of timestamped web documents related to the evolving topic, timeline summarization (TS) highlights its most important events in the form of relevant summaries to represent the development of a topic over time. Most of the previous ... More
A Dynamic Evolutionary Framework for Timeline Generation based on Distributed RepresentationsMay 14 2019Given the collection of timestamped web documents related to the evolving topic, timeline summarization (TS) highlights its most important events in the form of relevant summaries to represent the development of a topic over time. Most of the previous ... More
Multilingual Factor AnalysisMay 14 2019In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary. We model equivalent words in different languages as different views of the ... More
The Language of Legal and Illegal Activity on the DarknetMay 14 2019The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity. Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP ... More
Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification SettingMay 14 2019Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in CONCEPTNET, taking into account their specific properties, which can make relation classification difficult: a given concept ... More
Atom Responding Machine for Dialog GenerationMay 14 2019Recently, improving the relevance and diversity of dialogue system has attracted wide attention. For a post x, the corresponding response y is usually diverse in the real-world corpus, while the conventional encoder-decoder model tends to output the high-frequency ... More
Atom Responding Machine for Dialog GenerationMay 14 2019May 15 2019Recently, improving the relevance and diversity of dialogue system has attracted wide attention. For a post x, the corresponding response y is usually diverse in the real-world corpus, while the conventional encoder-decoder model tends to output the high-frequency ... More
Entity-Relation Extraction as Multi-Turn Question AnsweringMay 14 2019In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the task of identifying answer spans from ... More
Is Word Segmentation Necessary for Deep Learning of Chinese Representations?May 14 2019Segmenting a chunk of text into words is usually the first step of processing Chinese text, but its necessity has rarely been explored. In this paper, we ask the fundamental question of whether Chinese word segmentation (CWS) is necessary for deep learning-based ... More
Deep Residual Output Layers for Neural Language GenerationMay 14 2019Many tasks, including language generation, benefit from learning the structure of the output space, particularly when the space of output labels is large and the data is sparse. State-of-the-art neural language models indirectly capture the output space ... More
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared VocabulariesMay 14 2019Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pre-trained ... More
Improving Neural Conversational Models with Entropy-Based Data FilteringMay 14 2019Current neural-network based conversational models lack diversity and generate boring responses to open-ended utterances. Priors such as persona, emotion, or topic provide additional information to dialog models to aid response generation, but annotating ... More
Cognitive Graph for Multi-Hop Reading Comprehension at ScaleMay 14 2019We propose a new CogQA framework for multi-hop question answering in web-scale documents. Inspired by the dual process theory in cognitive science, the framework gradually builds a \textit{cognitive graph} in an iterative process by coordinating an implicit ... More
On the number of k-skip-n-gramsMay 14 2019The paper proves that the number of k-skip-n-grams for a corpus of size $L$ is $$\frac{Ln + n + k' - n^2 - nk'}{n} \cdot \binom{n-1+k'}{n-1}$$ where $k' = \min(L - n + 1, k)$.
A Survey of Multilingual Neural Machine TranslationMay 14 2019We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of knowledge transfer. MNMT is more promising and interesting ... More
Towards Content Transfer through Grounded Text GenerationMay 13 2019Recent work in neural generation has attracted significant interest in controlling the form of text, such as style, persona, and politeness. However, there has been less work on controlling neural text generation for content. This paper introduces the ... More
A Review of Keyphrase ExtractionMay 13 2019Automated keyphrase extraction is a crucial textual information processing task regarding the most types of digital content management systems. It concerns the selection of representative and characteristic phrases from a document that express all aspects ... More
Modelling Instance-Level Annotator Reliability for Natural Language Labelling TasksMay 13 2019When constructing models that learn from noisy labels produced by multiple annotators, it is important to accurately estimate the reliability of annotators. Annotators may provide labels of inconsistent quality due to their varying expertise and reliability ... More
Learning to Exploit Long-term Relational Dependencies in Knowledge GraphsMay 13 2019We study the problem of knowledge graph (KG) embedding. A widely-established assumption to this problem is that similar entities are likely to have similar relational roles. However, existing related methods derive KG embeddings mainly based on triple-level ... More
Quantifying and Alleviating the Language Prior Problem in Visual Question AnsweringMay 13 2019Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past ... More
Synchronous Bidirectional Neural Machine TranslationMay 13 2019Existing approaches to neural machine translation (NMT) generate the target language sequence token by token from left to right. However, this kind of unidirectional decoding framework cannot make full use of the target-side future contexts which can ... More
Transfer Learning for Scientific Data Chain Extraction in Small Chemical Corpus with BERT-CRF ModelMay 13 2019Computational chemistry develops fast in recent years due to the rapid growth and breakthroughs in AI. Thanks for the progress in natural language processing, researchers can extract more fine-grained knowledge in publications to stimulate the development ... More
Challenges in Building Intelligent Open-domain Dialog SystemsMay 13 2019There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, ... More
The Secret Lives of Names? Name Embeddings from Social MediaMay 12 2019Your name tells a lot about you: your gender, ethnicity and so on. It has been shown that name embeddings are more effective in representing names than traditional substring features. However, our previous name embedding model is trained on private email ... More
A Benchmark Study on Machine Learning Methods for Fake News DetectionMay 12 2019The proliferation of fake news and its propagation on social media have become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been attempted to detect it. However, most of those focused on ... More
A Comparison of Techniques for Sentiment Classification of Film ReviewsMay 12 2019We undertake the task of comparing lexicon-based sentiment classification of film reviews with machine learning approaches. We look at existing methodologies and attempt to emulate and improve on them using a 'given' lexicon and a bag-of-words approach. ... More
The relational processing limits of classic and contemporary neural network models of language processingMay 12 2019The ability of neural networks to capture relational knowledge is a matter of long-standing controversy. Recently, some researchers in the PDP side of the debate have argued that (1) classic PDP models can handle relational structure (Rogers & McClelland, ... More
Improving Natural Language Interaction with Robots Using AdviceMay 12 2019Over the last few years, there has been growing interest in learning models for physically grounded language understanding tasks, such as the popular blocks world domain. These works typically view this problem as a single-step process, in which a human ... More
Semantic categories of artifacts and animals reflect efficient codingMay 11 2019It has been argued that semantic categories across languages reflect pressure for efficient communication. Recently, this idea has been cast in terms of a general information-theoretic principle of efficiency, the Information Bottleneck (IB) principle, ... More
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker VerificationMay 11 2019There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV). ... More
Controlled Natural Languages and Default ReasoningMay 11 2019Controlled natural languages (CNLs) are effective languages for knowledge representation and reasoning. They are designed based on certain natural languages with restricted lexicon and grammar. CNLs are unambiguous and simple as opposed to their base ... More
Encrypted Speech Recognition using Deep Polynomial NetworksMay 11 2019The cloud-based speech recognition/API provides developers or enterprises an easy way to create speech-enabled features in their applications. However, sending audios about personal or company internal information to the cloud, raises concerns about the ... More
Using syntactical and logical forms to evaluate textual inference competenceMay 10 2019In the light of recent breakthroughs in transfer learning for Natural Language Processing, much progress was achieved on Natural Language Inference. Different models are now presenting high accuracy on popular inference datasets such as SNLI, MNLI and ... More
Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the WebMay 10 2019Over the past few years, we have been witnessing the rise of misinformation on the Web. People fall victims of fake news during their daily lives and assist their further propagation knowingly and inadvertently. There have been many initiatives that are ... More
Language Modeling with Deep TransformersMay 10 2019We explore multi-layer autoregressive Transformer models in language modeling for speech recognition. We focus on two aspects. First, we revisit Transformer model configurations specifically for language modeling. We show that well configured Transformer ... More
A New Anchor Word Selection Method for the Separable Topic DiscoveryMay 10 2019Separable Non-negative Matrix Factorization (SNMF) is an important method for topic modeling, where "separable" assumes every topic contains at least one anchor word, defined as a word that has non-zero probability only on that topic. SNMF focuses on ... More
Survey on Evaluation Methods for Dialogue SystemsMay 10 2019In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, ... More
Restoring Arabic vowels through omission-tolerant dictionary lookupMay 10 2019Vowels in Arabic are optional orthographic symbols written as diacritics above or below letters. In Arabic texts, typically more than 97 percent of written words do not explicitly show any of the vowels they contain; that is to say, depending on the author, ... More
Densifying Assumed-sparse Tensors: Improving Memory Efficiency and MPI Collective Performance during Tensor Accumulation for Parallelized Training of Neural Machine Translation ModelsMay 10 2019Neural machine translation - using neural networks to translate human language - is an area of active research exploring new neuron types and network topologies with the goal of dramatically improving machine translation performance. Current state-of-the-art ... More
Legal Judgment Prediction via Multi-Perspective Bi-Feedback NetworkMay 10 2019May 16 2019The Legal Judgment Prediction (LJP) is to determine judgment results based on the fact descriptions of the cases. LJP usually consists of multiple subtasks, such as applicable law articles prediction, charges prediction, and the term of the penalty prediction. ... More
Legal Judgment Prediction via Multi-Perspective Bi-Feedback NetworkMay 10 2019The Legal Judgment Prediction (LJP) is to determine judgment results based on the fact descriptions of the cases. LJP usually consists of multiple subtasks, such as applicable law articles prediction, charges prediction, and the term of the penalty prediction. ... More
MobiVSR: A Visual Speech Recognition Solution for Mobile DevicesMay 10 2019Visual speech recognition (VSR) is the task of recognizing spoken language from video input only, without any audio. VSR has many applications as an assistive technology, especially if it could be deployed in mobile devices and embedded systems. The need ... More
When Deep Learning Met Code SearchMay 09 2019There have been multiple recent proposals on using deep neural networks for code search using natural language. Common across these proposals is the idea of $\mathit{embedding}$ code and natural language queries, into real vectors and then using vector ... More
Mappa Mundi: An Interactive Artistic Mind Map Generator with Artificial ImaginationMay 09 2019We present a novel real-time, collaborative, and interactive AI painting system, Mappa Mundi, for artistic Mind Map creation. The system consists of a voice-based input interface, an automatic topic expansion module, and an image projection module. The ... More
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping SpeechMay 09 2019Significant performance degradation of automatic speech recognition (ASR) systems is observed when the audio signal contains cross-talk. One of the recently proposed approaches to solve the problem of multi-speaker ASR is the deep clustering (DPCL) approach. ... More
Targeted Sentiment Analysis: A Data-Driven CategorizationMay 09 2019Targeted sentiment analysis (TSA), also known as aspect based sentiment analysis (ABSA), aims at detecting fine-grained sentiment polarity towards targets in a given opinion document. Due to the lack of labeled datasets and effective technology, TSA had ... More
Confirmatory Factor Analysis -- A Case studyMay 08 2019Confirmatory Factor Analysis (CFA) is a particular form of factor analysis, most commonly used in social research. In confirmatory factor analysis, the researcher first develops a hypothesis about what factors they believe are underlying the used measures ... More
Unified Language Model Pre-training for Natural Language Understanding and GenerationMay 08 2019This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling objectives: unidirectional (both ... More
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention - w/o Data AugmentationMay 08 2019We present state-of-the-art automatic speech recognition (ASR) systems employing a standard hybrid DNN\/HMM architecture compared to an attention-based encoder-decoder design for the LibriSpeech task. Detailed descriptions of the system development, including ... More
On the Feasibility of Automated Detection of Allusive Text ReuseMay 08 2019The detection of allusive text reuse is particularly challenging due to the sparse evidence on which allusive references rely---commonly based on none or very few shared words. Arguably, lexical semantics can be resorted to since uncovering semantic relations ... More
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent AdvancesMay 08 2019Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing ... More
ShapeGlot: Learning Language for Shape DifferentiationMay 08 2019In this work we explore how fine-grained differences between the shapes of common objects are expressed in language, grounded on images and 3D models of the objects. We first build a large scale, carefully controlled dataset of human utterances that each ... More
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word RepresentationsMay 08 2019Syntax has been demonstrated highly effective in neural machine translation (NMT). Previous NMT models integrate syntax by representing 1-best tree outputs from a well-trained parsing system, e.g., the representative Tree-RNN and Tree-Linearization methods, ... More
Automatic Inference of Minimalist Grammars using an SMT-SolverMay 08 2019We introduce (1) a novel parser for Minimalist Grammars (MG), encoded as a system of first-order logic formulae that may be evaluated using an SMT-solver, and (2) a novel procedure for inferring Minimalist Grammars using this parser. The input to this ... More