Latest in cs.cl

total 14119took 0.12s
Facebook FAIR's WMT19 News Translation Task SubmissionJul 15 2019This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from last year, our baseline ... More
Asking Clarifying Questions in Open-Domain Information-Seeking ConversationsJul 15 2019Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating experience. Alternatively, systems can improve user ... More
Naver Labs Europe's Systems for the WMT19 Machine Translation Robustness TaskJul 15 2019This paper describes the systems that we submitted to the WMT19 Machine Translation robustness task. This task aims to improve MT's robustness to noise found on social media, like informal language, spelling mistakes and other orthographic variations. ... More
RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregationJul 15 2019Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems. We explore how load centrality, a graph-theoretic measure applied to graphs ... More
Investigation on N-gram Approximated RNNLMs for Recognition of Morphologically Rich SpeechJul 15 2019Recognition of Hungarian conversational telephone speech is challenging due to the informal style and morphological richness of the language. Recurrent Neural Network Language Model (RNNLM) can provide remedy for the high perplexity of the task; however, ... More
GLOSS: Generative Latent Optimization of Sentence RepresentationsJul 15 2019We propose a method to learn unsupervised sentence representations in a non-compositional manner based on Generative Latent Optimization. Our approach does not impose any assumptions on how words are to be combined into a sentence representation. We discuss ... More
Joint Language Identification of Code-Switching Speech using Attention based E2E NetworkJul 15 2019Language identification (LID) has relevance in many speech processing applications. For the automatic recognition of code-switching speech, the conventional approaches often employ an LID system for detecting the languages present within an utterance. ... More
Ranking sentences from product description & bullets for better searchJul 15 2019Products in an ecommerce catalog contain information-rich fields like description and bullets that can be useful to extract entities (attributes) using NER based systems. However, these fields are often verbose and contain lot of information that is not ... More
TWEETQA: A Social Media Focused Question Answering DatasetJul 14 2019With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge. While previous ... More
A Simple BERT-Based Approach for Lexical SimplificationJul 14 2019Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence ... More
Simple Automatic Post-editing for Arabic-Japanese Machine TranslationJul 14 2019A common bottleneck for developing machine translation (MT) systems for some language pairs is the lack of direct parallel translation data sets, in general and in certain domains. Alternative solutions such as zero-shot models or pivoting techniques ... More
Automatic Repair and Type Binding of Undeclared Variables using Neural NetworksJul 14 2019Deep learning had been used in program analysis for the prediction of hidden software defects using software defect datasets, security vulnerabilities using generative adversarial networks as well as identifying syntax errors by learning a trained neural ... More
Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine TranslationJul 14 2019This paper describes the Microsoft Translator submissions to the WMT19 news translation shared task for English-German. Our main focus is document-level neural machine translation with deep transformer models. We start with strong sentence-level baselines, ... More
Tackling Graphical NLP problems with Graph Recurrent NetworksJul 13 2019How to properly model graphs is a long-existing and important problem in NLP area, where several popular types of graphs are knowledge graphs, semantic graphs and dependency graphs. Comparing with other data structures, such as sequences and trees, graphs ... More
BUT VOiCES 2019 System DescriptionJul 13 2019This is a description of our effort in VOiCES 2019 Speaker Recognition challenge. All systems in the fixed condition are based on the x-vector paradigm with different features and DNN topologies. The single best system reaches 1.2% EER and a fusion of ... More
Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectorsJul 13 2019In this paper, we combine Hidden Markov Models (HMMs) with i-vector extractors to address the problem of text-dependent speaker recognition with random digit strings. We employ digit-specific HMMs to segment the utterances into digits, to perform frame ... More
Relational Memory-based Knowledge Graph EmbeddingJul 13 2019Knowledge graph embedding models often suffer from a limitation of remembering existing triples to predict new triples. To overcome this issue, we introduce a novel embedding model, named R-MeN, that explores a relational memory network to model relationship ... More
Cross-Lingual Transfer Learning for Question AnsweringJul 13 2019Deep learning based question answering (QA) on English documents has achieved success because there is a large amount of English training examples. However, for most languages, training examples for high-quality QA models are not available. In this paper, ... More
Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech RecognitionJul 13 2019Integrating an external language model into a sequence-to-sequence speech recognition system is non-trivial. Previous works utilize linear interpolation or a fusion network to integrate external language models. However, these approaches introduce external ... More
Pykaldi2: Yet another speech toolkit based on Kaldi and PytorchJul 12 2019We introduce PyKaldi2 speech recognition toolkit implemented based on Kaldi and PyTorch. While similar toolkits are available built on top of the two, a key feature of PyKaldi2 is sequence training with criteria such as MMI, sMBR and MPE. In particular, ... More
The University of Edinburgh's Submissions to the WMT19 News Translation TaskJul 12 2019The University of Edinburgh participated in the WMT19 Shared Task on News Translation in six language directions: English-to-Gujarati, Gujarati-to-English, English-to-Chinese, Chinese-to-English, German-to-English, and English-to-Czech. For all translation ... More
Equiprobable mappings in weighted constraint grammarsJul 12 2019We show that MaxEnt is so rich that it can distinguish between any two different mappings: there always exists a nonnegative weight vector which assigns them different MaxEnt probabilities. Stochastic HG instead does admit equiprobable mappings and we ... More
Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue SystemsJul 12 2019Data scarcity is a long-standing and crucial challenge that hinders quick development of task-oriented dialogue systems across multiple domains: task-oriented dialogue models are expected to learn grammar, syntax, dialogue reasoning, decision making, ... More
Automated Word Stress Detection in RussianJul 12 2019In this study we address the problem of automated word stress detection in Russian using character level models and no part-speech-taggers. We use a simple bidirectional RNN with LSTM nodes and achieve the accuracy of 90% or higher. We experiment with ... More
Justifying Diagnosis Decisions by Deep Neural NetworksJul 12 2019An integrated approach is proposed across visual and textual data to both determine and justify a medical diagnosis by a neural network. As deep learning techniques improve, interest grows to apply them in medical applications. To enable a transition ... More
Saliency Maps Generation for Automatic Text SummarizationJul 12 2019Saliency map generation techniques are at the forefront of explainable AI literature for a broad range of machine learning applications. Our goal is to question the limits of these approaches on more complex tasks. In this paper we apply Layer-Wise Relevance ... More
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity RecognitionJul 12 2019The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM). However, RNNs are limited by their recurrent nature in terms of computational efficiency. In contrast, ... More
Effective Incorporation of Speaker Information in Utterance Encoding in DialogJul 12 2019In dialog studies, we often encode a dialog using a hierarchical encoder where each utterance is converted into an utterance vector, and then a sequence of utterance vectors is converted into a dialog vector. Since knowing who produced which utterance ... More
Neural News Recommendation with Attentive Multi-View LearningJul 12 2019Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods ... More
R-Transformer: Recurrent Neural Network Enhanced TransformerJul 12 2019Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, ... More
ScenarioSA: A Large Scale Conversational Database for Interactive Sentiment AnalysisJul 12 2019Interactive sentiment analysis is an emerging, yet challenging, subtask of the sentiment analysis problem. It aims to discover the affective state and sentimental change of each person in a conversation. Existing sentiment analysis approaches are insufficient ... More
NPA: Neural News Recommendation with Personalized AttentionJul 12 2019News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article ... More
The Dynamic Embedded Topic ModelJul 12 2019Topic modeling analyzes documents to learn meaningful patterns of words. Dynamic topic models capture how these patterns vary over time for a set of documents that were collected over a large time span. We develop the dynamic embedded topic model (D-ETM), ... More
Solving Hard Coreference ProblemsJul 11 2019Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and ... More
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningJul 11 2019We present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language. Using DSTC2 as seed data, we trained natural language understanding (NLU) and generation (NLG) networks for each agent ... More
Effective and General Evaluation for Instruction Conditioned Navigation using Dynamic Time WarpingJul 11 2019In instruction conditioned navigation, agents interpret natural language and their surroundings to navigate through an environment. Datasets for studying this task typically contain pairs of these instructions and reference trajectories. Yet, most evaluation ... More
Incrementalizing RASA's Open-Source Natural Language Understanding PipelineJul 11 2019As spoken dialogue systems and chatbots are gaining more widespread adoption, commercial and open-sourced services for natural language understanding are emerging. In this paper, we explain how we altered the open-source RASA natural language understanding ... More
Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog SystemsJul 11 2019Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e.g. flight booking, hotel reservation, technical support, student advising etc. ... More
Self-Regulated Interactive Sequence-to-Sequence LearningJul 11 2019Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) ... More
Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in VideosJul 11 2019Contextual reasoning is essential to understand events in long untrimmed videos. In this work, we systematically explore different captioning models with various contexts for the dense-captioning events in video task, which aims to generate captions for ... More
MeetUp! A Corpus of Joint Activity Dialogues in a Visual EnvironmentJul 11 2019Building computer systems that can converse about their visual environment is one of the oldest concerns of research in Artificial Intelligence and Computational Linguistics (see, for example, Winograd's 1972 SHRDLU system). Only recently, however, have ... More
No Word is an Island -- A Transformation Weighting Model for Semantic CompositionJul 11 2019Composition models of distributional semantics are used to construct phrase representations from the representations of their words. Composition models are typically situated on two ends of a spectrum. They either have a small number of parameters but ... More
Massively Multilingual Neural Machine Translation in the Wild: Findings and ChallengesJul 11 2019We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. We set a milestone towards this goal by building a single massively multilingual NMT model handling 103 languages ... More
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from WikipediaJul 10 2019We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of WikiPedia articles in 85 languages, including several dialects or low-resource languages. We do not limit the the extraction ... More
Vision-and-Dialog NavigationJul 10 2019Robots navigating in human environments should use language to ask for assistance and be able to understand human responses. To study this challenge, we introduce Cooperative Vision-and-Dialog Navigation, a dataset of over 2k embodied, human-human dialogs ... More
Modelling the Socialization of Creative Agents in a Master-Apprentice Setting: The Case of Movie Title PunsJul 10 2019This paper presents work on modelling the social psychological aspect of socialization in the case of a computationally creative master-apprentice system. In each master-apprentice pair, the master, a genetic algorithm, is seen as a parent for its apprentice, ... More
Can Unconditional Language Models Recover Arbitrary Sentences?Jul 10 2019Neural network-based generative language models like ELMo and BERT can work effectively as general purpose sentence encoders in text classification without further fine-tuning. Is it possible to adapt them in a similar way for use as general-purpose decoders? ... More
Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech RecognitionJul 10 2019In automatic speech recognition (ASR), wideband (WB) and narrowband (NB) speech signals with different sampling rates typically use separate acoustic models. Therefore mixed-bandwidth (MB) acoustic modeling has important practical values for ASR system ... More
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech RecognitionJul 10 2019Evolutionary stochastic gradient descent (ESGD) was proposed as a population-based approach that combines the merits of gradient-aware and gradient-free optimization algorithms for superior overall optimization performance. In this paper we investigate ... More
BAM! Born-Again Multi-Task Networks for Natural Language UnderstandingJul 10 2019It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training ... More
ReQA: An Evaluation for End-to-End Answer Retrieval ModelsJul 10 2019Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance. However, retrieving relevant answers from a huge corpus of documents is still a challenging ... More
Large Memory Layers with Product KeysJul 10 2019This paper introduces a structured memory which can be easily integrated into a neural network. The memory is very large by design and therefore significantly increases the capacity of the architecture, by up to a billion parameters with a negligible ... More
Modeling Semantic Compositionality with Sememe KnowledgeJul 10 2019Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works ... More
Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric SpeechJul 10 2019This paper proposed a novel approach for the detection and reconstruction of dysarthric speech. The encoder-decoder model factorizes speech into a low-dimensional latent space and encoding of the input text. We showed that the latent space conveys interpretable ... More
A Modular Task-oriented Dialogue System Using a Neural Mixture-of-ExpertsJul 10 2019End-to-end Task-oriented Dialogue Systems (TDSs) have attracted a lot of attention for their superiority (e.g., in terms of global optimization) over pipeline modularized TDSs. Previous studies on end-to-end TDSs use a single-module model to generate ... More
Lingua Custodia at WMT'19: Attempts to Control TerminologyJul 10 2019This paper describes Lingua Custodia's submission to the WMT'19 news shared task for German-to-French on the topic of the EU elections. We report experiments on the adaptation of the terminology of a machine translation system to a specific topic, aimed ... More
Neural Networks as Explicit Word-Based RulesJul 10 2019Filters of convolutional networks used in computer vision are often visualized as image patches that maximize the response of the filter. We use the same approach to interpret weight matrices in simple architectures for natural language processing tasks. ... More
Semantic Parsing with Dual LearningJul 10 2019Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. In this work, we develop a semantic parsing framework with the dual learning algorithm, ... More
Exploiting user-frequency information for mining regionalisms from Social Media textsJul 10 2019The task of detecting regionalisms (expressions or words used in certain regions) has traditionally relied on the use of questionnaires and surveys, and has also heavily depended on the expertise and intuition of the surveyor. The irruption of Social ... More
Multi-Speaker End-to-End Speech SynthesisJul 09 2019In this work, we extend ClariNet (Ping et al., 2019), a fully end-to-end speech synthesis model (i.e., text-to-wave), to generate high-fidelity speech from multiple speakers. To model the unique characteristic of different voices, low dimensional trainable ... More
Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice CloningJul 09 2019We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages. Moreover, the model is able to transfer voices across languages, e.g. synthesize fluent Spanish ... More
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language ProcessingJul 09 2019We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate ... More
Sentiment Analysis Challenges in Persian LanguageJul 09 2019The rapid growth in data on the internet requires a data mining process to reach a decision to support insight. The Persian language has strong potential for deep research in any aspect of natural language processing, especially sentimental analysis approach. ... More
On Adversarial Removal of Hypothesis-only Bias in Natural Language InferenceJul 09 2019Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be ... More
Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language InferenceJul 09 2019Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models ... More
M3D-GAN: Multi-Modal Multi-Domain Translation with Universal AttentionJul 09 2019Generative adversarial networks have led to significant advances in cross-modal/domain translation. However, typically these networks are designed for a specific task (e.g., dialogue generation or image synthesis, but not both). We present a unified model, ... More
Transfer Learning from Audio-Visual Grounding to Speech RecognitionJul 09 2019Transfer learning aims to reduce the amount of data required to excel at a new task by re-using the knowledge acquired from learning other related tasks. This paper proposes a novel transfer learning scenario, which distills robust phonetic features from ... More
Cross-Domain Generalization of Neural Constituency ParsersJul 09 2019Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing -- but to what degree do they generalize to other domains? We present three results about the generalization of neural parsers in a zero-shot setting: training ... More
Multilingual Universal Sentence Encoder for Semantic RetrievalJul 09 2019We introduce two pre-trained retrieval focused multilingual sentence encoding models, respectively based on the Transformer and CNN model architectures. The models embed text from 16 languages into a single semantic space using a multi-task trained dual-encoder ... More
UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language InferenceJul 09 2019Recent advances in distributed language modeling have led to large performance increases on a variety of natural language processing (NLP) tasks. However, it is not well understood how these methods may be augmented by knowledge-based approaches. This ... More
Translating neural signals to text using a Brain-Machine InterfaceJul 09 2019Brain-Computer Interfaces (BCI) help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech output by direct neural processing. However, practical implementation of such a system has proven difficult due ... More
Improving the Performance of the LSTM and HMM Models via HybridizationJul 09 2019Language models based on deep neural neural networks and traditionalstochastic modelling has become both highly functional and effective in recenttimes. In this work a general survey into the two types of language modelling is conducted. We investigate ... More
Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech RecognitionJul 09 2019End-to-end neural network systems for automatic speech recognition (ASR) are trained from acoustic features to text transcriptions. In contrast to modular ASR systems, which contain separately-trained components for acoustic modeling, pronunciation lexicon, ... More
Sequence-to-Sequence Natural Language to Humanoid Robot Sign LanguageJul 09 2019This paper presents a study on natural language to sign language translation with human-robot interaction application purposes. By means of the presented methodology, the humanoid robot TEO is expected to represent Spanish sign language automatically ... More
Sentiment and position-taking analysis of parliamentary debates: A systematic literature reviewJul 09 2019Parliamentary and legislative debate transcripts provide access to information concerning the opinions, positions and policy preferences of elected politicians. They attract attention from researchers from a wide variety of backgrounds, from political ... More
Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective FunctionJul 09 2019Translation-based embedding models have gained significant attention in link prediction tasks for knowledge graphs. TransE is the primary model among translation-based embeddings and is well-known for its low complexity and high efficiency. Therefore, ... More
On the Semantic Interpretability of Artificial Intelligence ModelsJul 09 2019Artificial Intelligence models are becoming increasingly more powerful and accurate, supporting or even replacing humans' decision making. But with increased power and accuracy also comes higher complexity, making it hard for users to understand how the ... More
Multitask Learning for Blackmarket Tweet DetectionJul 09 2019Online social media platforms have made the world more connected than ever before, thereby making it easier for everyone to spread their content across a wide variety of audiences. Twitter is one such popular platform where people publish tweets to spread ... More
Teach an all-rounder with experts in different domainsJul 09 2019In many automatic speech recognition (ASR) tasks, an ideal model has to be applicable over multiple domains. In this paper, we propose to teach an all-rounder with experts in different domains. Concretely, we build a multi-domain acoustic model by applying ... More
To Tune or Not To Tune? How About the Best of Both Worlds?Jul 09 2019The introduction of pre-trained language models has revolutionized natural language research communities. However, researchers still know relatively little regarding their theoretical and empirical properties. In this regard, Peters et al. perform several ... More
Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on MobileJul 09 2019Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence ... More
ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue GenerationJul 09 2019In multi-turn dialogue generation, response is usually related with only a few contexts. Therefore, an ideal model should be able to detect these relevant contexts and produce a suitable response accordingly. However, the widely used hierarchical recurrent ... More
Implicit Discourse Relation Identification for Open-domain DialoguesJul 09 2019Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system. Previous work primarily ... More
Systematic quantitative analyses reveal the folk-zoological knowledge embedded in folktalesJul 09 2019Cultural learning is a unique human capacity essential for a wide range of adaptations. Researchers have argued that folktales have the pedagogical function of transmitting the essential information for the environment. The most important knowledge for ... More
Learning by Abstraction: The Neural State MachineJul 09 2019We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic views of AI and integrate their complementary strengths for the task of visual reasoning. Given an image, we first predict a probabilistic graph that represents ... More
Learning by Abstraction: The Neural State MachineJul 09 2019Jul 11 2019We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic views of AI and integrate their complementary strengths for the task of visual reasoning. Given an image, we first predict a probabilistic graph that represents ... More
Learning by Abstraction: The Neural State MachineJul 09 2019Jul 15 2019We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic views of AI and integrate their complementary strengths for the task of visual reasoning. Given an image, we first predict a probabilistic graph that represents ... More
NTT's Machine Translation Systems for WMT19 Robustness TaskJul 09 2019This paper describes NTT's submission to the WMT19 robustness task. This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulties from typical translation tasks such as news. Our submission combined ... More
Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellingsJul 09 2019Stretched words like `heellllp' or `heyyyyy' are a regular feature of spoken language, often used to emphasize or exaggerate the underlying meaning of the root word. While stretched words are rarely found in formal written language and dictionaries, they ... More
Joint Speech Recognition and Speaker Diarization via Sequence TransductionJul 09 2019Speech applications dealing with conversations require not only recognizing the spoken words, but also determining who spoke when. The task of assigning words to speakers is typically addressed by merging the outputs of two separate systems, namely, an ... More
An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation ArchitecturesJul 08 2019Earlier approaches indirectly studied the information captured by the hidden states of recurrent and non-recurrent neural machine translation models by feeding them into different classifiers. In this paper, we look at the encoder hidden states of both ... More
Predicting Customer Call Intent by Analyzing Phone Call Transcripts based on CNN for Multi-Class ClassificationJul 08 2019Auto dealerships receive thousands of calls daily from customers who are interested in sales, service, vendors and jobseekers. With so many calls, it is very important for auto dealers to understand the intent of these calls to provide positive customer ... More
Listen, Attend, Spell and Adapt: Speaker Adapted Sequence-to-Sequence ASRJul 08 2019Sequence-to-sequence (seq2seq) based ASR systems have shown state-of-the-art performances while having clear advantages in terms of simplicity. However, comparisons are mostly done on speaker independent (SI) ASR systems, though speaker adapted conventional ... More
Knowledge-aware Pronoun Coreference ResolutionJul 08 2019Resolving pronoun coreference requires knowledge support, especially for particular domains (e.g., medicine). In this paper, we explore how to leverage different types of knowledge to better resolve pronoun coreference with a neural model. To ensure the ... More
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue AgentJul 08 2019Multiple sequence to sequence models were used to establish an end-to-end multi-turns proactive dialogue generation agent, with the aid of data augmentation techniques and variant encoder-decoder structure designs. A rank-based ensemble approach was developed ... More
Early Discovery of Emerging Entities in MicroblogsJul 08 2019Keeping up to date on emerging entities that appear every day is indispensable for various applications, such as social-trend analysis and marketing research. Previous studies have attempted to detect unseen entities that are not registered in a particular ... More
Searching for Effective Neural Extractive Summarization: What Works and What's NextJul 08 2019The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of \textit{why} they perform so well, or \textit{how} they might be improved. In this paper, we seek to better ... More
Correct-and-Memorize: Learning to Translate from Interactive RevisionsJul 08 2019State-of-the-art machine translation models are still not on par with human translators. Previous work takes human interactions into the neural machine translation process to obtain improved results in target languages. However, not all model-translation ... More
A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable ContextJul 08 2019Common grounding is the process of creating, repairing and updating mutual understandings, which is a critical aspect of sophisticated human communication. However, traditional dialogue systems have limited capability of establishing common ground, and ... More
Topic Modeling in Embedding SpacesJul 08 2019Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the Embedded Topic Model (ETM), a ... More