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Fine-Tuning Language Models from Human PreferencesSep 18 2019Reward learning enables the application of reinforcement learning (RL) to tasks where reward is defined by human judgment, building a model of reward by asking humans questions. Most work on reward learning has used simulated environments, but complex ... More
Hierarchical Meta-Embeddings for Code-Switching Named Entity RecognitionSep 18 2019In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mainly focused on word-level aspects such as word embeddings. However, in ... More
Word Recognition, Competition, and Activation in a Model of Visually Grounded SpeechSep 18 2019In this paper, we study how word-like units are represented and activated in a recurrent neural model of visually grounded speech. The model used in our experiments is trained to project an image and its spoken description in a common representation space. ... More
Simple, Scalable Adaptation for Neural Machine TranslationSep 18 2019Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains. However, fine-tuning requires adapting and maintaining a separate model for each target task. We propose a simple yet efficient ... More
Improving Natural Language Inference with a Pretrained ParserSep 18 2019We introduce a novel approach to incorporate syntax into natural language inference (NLI) models. Our method uses contextual token-level vector representations from a pretrained dependency parser. Like other contextual embedders, our method is broadly ... More
Extracting evidence of supplement-drug interactions from literatureSep 17 2019Dietary supplements are used by a large portion of the population, but information on their safety is hard to find. We demonstrate an automated method for extracting evidence of supplement-drug interactions (SDIs) and supplement-supplement interactions ... More
Semantic Relatedness Based Re-ranker for Text SpottingSep 17 2019Applications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based neural approaches. We present ... More
Do NLP Models Know Numbers? Probing Numeracy in EmbeddingsSep 17 2019The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other tokens---they embed them as distributed vectors. Is this enough to capture ... More
Do NLP Models Know Numbers? Probing Numeracy in EmbeddingsSep 17 2019Sep 18 2019The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other tokens---they embed them as distributed vectors. Is this enough to capture ... More
Ludwig: a type-based declarative deep learning toolboxSep 17 2019In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code. Ludwig implements a novel approach to deep learning model building ... More
Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite PronounsSep 17 2019Computational research on error detection in second language speakers has mainly addressed clear grammatical anomalies typical to learners at the beginner-to-intermediate level. We focus instead on acquisition of subtle semantic nuances of English indefinite ... More
Learning to Deceive with Attention-Based ExplanationsSep 17 2019Attention mechanisms are ubiquitous components in neural architectures applied in natural language processing. In addition to yielding gains in predictive accuracy, researchers often claim that attention weights confer interpretability, purportedly useful ... More
Pointer-based Fusion of Bilingual Lexicons into Neural Machine TranslationSep 17 2019Neural machine translation (NMT) systems require large amounts of high quality in-domain parallel corpora for training. State-of-the-art NMT systems still face challenges related to out-of-vocabulary words and dealing with low-resource language pairs. ... More
Estimating Glycemic Impact of Cooking Recipes via Online Crowdsourcing and Machine LearningSep 17 2019Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels. However, laboratory analysis of dietary glycemic potency is time-consuming and expensive. In this paper, ... More
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced ModelSep 17 2019Recently, generating adversarial examples has become an important means of measuring robustness of a deep learning model. Adversarial examples help us identify the susceptibilities of the model and further counter those vulnerabilities by applying adversarial ... More
Character-Centric StorytellingSep 17 2019Sequential vision-to-language or visual storytelling has recently been one of the areas of focus in computer vision and language modeling domains. Though existing models generate narratives that read subjectively well, there could be cases when these ... More
Span-based Joint Entity and Relation Extraction with Transformer Pre-trainingSep 17 2019We introduce SpERT, an attention model for span-based joint entity and relation extraction. Our approach employs the pre-trained Transformer network BERT as its core. We use BERT embeddings as shared inputs for a light-weight reasoning, which features ... More
Multi Sense Embeddings from Topic ModelsSep 17 2019Distributed word embeddings have yielded state-of-the-art performance in many NLP tasks, mainly due to their success in capturing useful semantic information. These representations assign only a single vector to each word whereas a large number of words ... More
Course Concept Expansion in MOOCs with External Knowledge and Interactive GameSep 17 2019As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drifts and lack of knowledge guidance, existing methods can not effectively ... More
SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and ChatsSep 17 2019We present an overview of the EmotionX 2019 Challenge, held at the 7th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with IJCAI 2019. The challenge entailed predicting emotions in spoken and chat-based ... More
K-BERT: Enabling Language Representation with Knowledge GraphSep 17 2019Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines ... More
Multi-step Entity-centric Information Retrieval for Multi-Hop Question AnsweringSep 17 2019Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that ... More
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question AnsweringSep 17 2019A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain QA typically relies on off-the-shelf information retrieval (IR) techniques ... More
Learning Explicit and Implicit Structures for Targeted Sentiment AnalysisSep 17 2019Targeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information. Existing research efforts mostly regard this joint task as a sequence labeling problem, building models that can capture explicit ... More
Grounding learning of modifier dynamics: An application to color namingSep 17 2019Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as 'dirty blue'. We present a model of color modifiers that, compared with previous additive models in RGB space, learns more ... More
Inverse Visual Question Answering with Multi-Level AttentionsSep 17 2019In this paper, we propose a novel deep multi-level attention model to address inverse visual question answering. The proposed model generates regional visual and semantic features at the object level and then enhances them with the answer cue by using ... More
Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech TranslationSep 17 2019End-to-end speech translation, a hot topic in recent years, aims to translate a segment of audio into a specific language with an end-to-end model. Conventional approaches employ multi-task learning and pre-training methods for this task, but they suffer ... More
Discovering Differential Features: Adversarial Learning for Information Credibility EvaluationSep 16 2019A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet. However, these extracted features still suffer from many irrelevant and noisy features that restrict severely the performance of the ... More
Probing Natural Language Inference Models through Semantic FragmentsSep 16 2019Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about word substitutions in sentential ... More
Short-Text Classification Using Unsupervised Keyword ExpansionSep 16 2019Short-text classification, like all data science, struggles to achieve high performance using limited data. As a solution, a short sentence may be expanded with new and relevant feature words to form an artificially enlarged dataset, and add new features ... More
Automatic Detection and Classification of Cognitive Distortions in Mental Health TextSep 16 2019In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, leading ... More
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck PrincipleSep 16 2019The principle of the Information Bottleneck (Tishby et al. 1999) is to produce a summary of information X optimized to predict some other relevant information Y. In this paper, we propose a novel approach to unsupervised sentence summarization by mapping ... More
Multilingual Neural Machine Translation for Zero-Resource LanguagesSep 16 2019In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine translation (MT). However, NMT systems are limited in translating low-resourced ... More
Communication-based Evaluation for Natural Language GenerationSep 16 2019Natural language generation (NLG) systems are commonly evaluated using n-gram overlap measures (e.g. BLEU, ROUGE). These measures do not directly capture semantics or speaker intentions, and so they often turn out to be misaligned with our true goals ... More
Fast transcription of speech in low-resource languagesSep 16 2019We present software that, in only a few hours, transcribes forty hours of recorded speech in a surprise language, using only a few tens of megabytes of noisy text in that language, and a zero-resource grapheme to phoneme (G2P) table. A pretrained acoustic ... More
Prediction Uncertainty Estimation for Hate Speech ClassificationSep 16 2019As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased. Due to its harmful effect on minority groups as well as on large communities, there is a pressing need for hate speech detection and filtering. ... More
Domain Transfer in Dialogue Systems without Turn-Level SupervisionSep 16 2019Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation. State-of-the-art DST models are typically trained in a supervised manner from manual annotations ... More
Controllable Text-to-Image GenerationSep 16 2019In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language descriptions. To ... More
Global Autoregressive Models for Data-Efficient Sequence LearningSep 16 2019Standard autoregressive seq2seq models are easily trained by max-likelihood, but tend to show poor results under small-data conditions. We introduce a class of seq2seq models, GAMs (Global Autoregressive Models), which combine an autoregressive component ... More
Automatic detection of surgical site infections from a clinical data warehouseSep 16 2019Reducing the incidence of surgical site infections (SSIs) is one of the objectives of the French nosocomial infection control program. Manual monitoring of SSIs is carried out each year by the hospital hygiene team and surgeons at the University Hospital ... More
Bridging the domain gap in cross-lingual document classificationSep 16 2019The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages. Recent developments in cross-lingual understanding (XLU) has made progress in this area, trying to bridge the language barrier using language ... More
KorQuAD1.0: Korean QA Dataset for Machine Reading ComprehensionSep 16 2019Sep 17 2019Machine Reading Comprehension (MRC) is a task that requires machine to understand natural language and answer questions by reading a document. It is the core of automatic response technology such as chatbots and automatized customer supporting systems. ... More
KorQuAD1.0: Korean QA Dataset for Machine Reading ComprehensionSep 16 2019Machine Reading Comprehension (MRC) is a task that requires machine to understand natural language and answer questions by reading a document. It is the core of automatic response technology such as chatbots and automatized customer supporting systems. ... More
CM-Net: A Novel Collaborative Memory Network for Spoken Language UnderstandingSep 16 2019Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works. However, most existing models fail to fully utilize co-occurrence relations between slots and intents, ... More
Temporal Self-Attention Network for Medical Concept EmbeddingSep 15 2019In longitudinal electronic health records (EHRs), the event records of a patient are distributed over a long period of time and the temporal relations between the events reflect sufficient domain knowledge to benefit prediction tasks such as the rate ... More
Query-Focused Scenario ConstructionSep 15 2019The news coverage of events often contains not one but multiple incompatible accounts of what happened. We develop a query-based system that extracts compatible sets of events (scenarios) from such data, formulated as one-class clustering. Our system ... More
Human Language: A Boson Gas of Quantum Entangled CognitonsSep 15 2019We model a piece of text of human language telling a story by means of the quantum structure describing a Bose gas in a temperature close to a Bose-Einstein condensate near absolute zero. For this we introduce energy levels for the concepts (words) used ... More
Automatically Extracting Challenge Sets for Non-local Phenomena Neural Machine TranslationSep 15 2019We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model. Since most dependencies ... More
Voice Conversion Using Cycle-Consistent Variational AutoencoderSep 15 2019One of the most critical obstacles in voice conversion is the requirement of parallel training data, which contain the same linguistic content utterances spoken by different speakers. Collecting such parallel data is highly expensive process, therefore ... More
Cross-Lingual BERT Transformation for Zero-Shot Dependency ParsingSep 15 2019This paper investigates the problem of learning cross-lingual representations in a contextual space. We propose Cross-Lingual BERT Transformation (CLBT), a simple and efficient approach to generate cross-lingual contextualized word embeddings based on ... More
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB RetrieverSep 15 2019Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue generation work treats the KB query as an attention over the entire KB, without the guarantee ... More
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB RetrieverSep 15 2019Sep 18 2019Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue generation work treats the KB query as an attention over the entire KB, without the guarantee ... More
Learning Rhyming Constraints using Structured AdversariesSep 15 2019Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry. Much prior work on poetry generation uses manually defined constraints which are satisfied during ... More
Emu: Enhancing Multilingual Sentence Embeddings with Semantic SpecializationSep 15 2019We present Emu, a system that semantically enhances multilingual sentence embeddings. Our framework fine-tunes pre-trained multilingual sentence embeddings using two main components: a semantic classifier and a language discriminator. The semantic classifier ... More
Natural Language Adversarial Attacks and Defenses in Word LevelSep 15 2019Up until recent two years, inspired by the big amount of research about adversarial example in the field of computer vision, there has been a growing interest in adversarial attacks for Natural Language Processing (NLP). What followed was a very few works ... More
Hint-Based Training for Non-Autoregressive Machine TranslationSep 15 2019Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer from high inference latency. Non-AutoRegressive Translation (NART) models ... More
Ouroboros: On Accelerating Training of Transformer-Based Language ModelsSep 14 2019Language models are essential for natural language processing (NLP) tasks, such as machine translation and text summarization. Remarkable performance has been demonstrated recently across many NLP domains via a Transformer-based language model with over ... More
Beyond BLEU: Training Neural Machine Translation with Semantic SimilaritySep 14 2019While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can substantially improve final translation ... More
Delivering Cognitive Behavioral Therapy Using A Conversational SocialRobotSep 14 2019Social robots are becoming an integrated part of our daily life due to their ability to provide companionship and entertainment. A subfield of robotics, Socially Assistive Robotics (SAR), is particularly suitable for expanding these benefits into the ... More
musicnn: Pre-trained convolutional neural networks for music audio taggingSep 14 2019Pronounced as "musician", the musicnn library contains a set of pre-trained musically motivated convolutional neural networks for music audio tagging: This repository also includes some pre-trained vgg-like baselines. ... More
Current Challenges in Spoken Dialogue Systems and Why They Are Critical for Those Living with DementiaSep 14 2019Dialogue technologies such as Amazon's Alexa have the potential to transform the healthcare industry. However, current systems are not yet naturally interactive: they are often turn-based, have naive end-of-turn detection and completely ignore many types ... More
Joint Wasserstein Autoencoders for Aligning Multimodal EmbeddingsSep 14 2019One of the key challenges in learning joint embeddings of multiple modalities, e.g. of images and text, is to ensure coherent cross-modal semantics that generalize across datasets. We propose to address this through joint Gaussian regularization of the ... More
Efficiency Metrics for Data-Driven Models: A Text Summarization Case StudySep 14 2019Using data-driven models for solving text summarization or similar tasks has become very common in the last years. Yet most of the studies report basic accuracy scores only, and nothing is known about the ability of the proposed models to improve when ... More
Integrating Source-channel and Attention-based Sequence-to-sequence Models for Speech RecognitionSep 14 2019This paper proposes a novel automatic speech recognition (ASR) framework called Integrated Source-Channel and Attention (ISCA) that combines the advantages of traditional systems based on the noisy source-channel model (SC) and end-to-end style systems ... More
ALTER: Auxiliary Text Rewriting Tool for Natural Language GenerationSep 14 2019In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewriting, and text style transfer. Our tool ... More
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word EmbeddingsSep 14 2019Sep 17 2019Though word embeddings and topics are complementary representations, several past works have only used pre-trained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents. However, no ... More
Multi-view and Multi-source Transfers in Neural Topic ModelingSep 14 2019Though word embeddings and topics are complementary representations, several past works have only used pre-trained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents. However, no ... More
Bootstrapping non-parallel voice conversion from speaker-adaptive text-to-speechSep 14 2019Voice conversion (VC) and text-to-speech (TTS) are two tasks that share a similar objective, generating speech with a target voice. However, they are usually developed independently under vastly different frameworks. In this paper, we propose a methodology ... More
Multilingual ASR with Massive Data AugmentationSep 14 2019Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the training data by creating acoustic variations. In this work we present a ... More
A Universal Parent Model for Low-Resource Neural Machine Translation TransferSep 14 2019Transfer learning from a high-resource language pair `parent' has been proven to be an effective way to improve neural machine translation quality for low-resource language pairs `children.' However, previous approaches build a custom parent model or ... More
Leveraging Out-of-Task Data for End-to-End Automatic Speech TranslationSep 14 2019For automatic speech translation (AST), end-to-end approaches are outperformed by cascaded models that transcribe with automatic speech recognition (ASR), then translate with machine translation (MT). A major cause of the performance gap is that, while ... More
Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag SetSep 14 2019Wikipedia is a great source of general world knowledge which can guide NLP models better understand their motivation to make predictions. We aim to create a large set of structured knowledge, usable for NLP models, from Wikipedia. The first step we take ... More
Learning Household Task Knowledge from WikiHow DescriptionsSep 13 2019Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment. While previous attempts at constructing procedural knowledge are mostly rule- and template-based, recent advances in deep learning provide the ... More
Addressing Semantic Drift in Question Generation for Semi-Supervised Question AnsweringSep 13 2019Text-based Question Generation (QG) aims at generating natural and relevant questions that can be answered by a given answer in some context. Existing QG models suffer from a "semantic drift" problem, i.e., the semantics of the model-generated question ... More
Probing the Information Encoded in x-vectorsSep 13 2019Deep neural network based speaker embeddings, such as x-vectors, have been shown to perform well in text-independent speaker recognition/verification tasks. In this paper, we use simple classifiers to investigate the contents encoded by x-vector embeddings. ... More
simple but effective techniques to reduce biasesSep 13 2019There have been several studies recently showing that strong natural language inference (NLI) models are prone to relying on unwanted dataset biases, resulting in models which fail to capture the underlying generalization, and are likely to perform poorly ... More
A Comparative Study on Transformer vs RNN in Speech ApplicationsSep 13 2019Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS). This paper focuses on an emergent sequence-to-sequence model called ... More
Toward Automated Quest Generation in Text-Adventure GamesSep 13 2019Interactive fictions, or text-adventures, are games in which a player interacts with a world entirely through textual descriptions and text actions. Text-adventure games are typically structured as puzzles or quests wherein the player must execute certain ... More
Parameterized Convolutional Neural Networks for Aspect Level Sentiment ClassificationSep 13 2019We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN). Experiments demonstrate ... More
Scene Graph Parsing by Attention GraphSep 13 2019Scene graph representations, which form a graph of visual object nodes together with their attributes and relations, have proved useful across a variety of vision and language applications. Recent work in the area has used Natural Language Processing ... More
Taxonomical hierarchy of canonicalized relations from multiple Knowledge BasesSep 13 2019This work addresses two important questions pertinent to Relation Extraction (RE). First, what are all possible relations that could exist between any two given entity types? Second, how do we define an unambiguous taxonomical (is-a) hierarchy among the ... More
End-to-End Neural Speaker Diarization with Self-attentionSep 13 2019Speaker diarization has been mainly developed based on the clustering of speaker embeddings. However, the clustering-based approach has two major problems; i.e., (i) it is not optimized to minimize diarization errors directly, and (ii) it cannot handle ... More
A Neural Approach to Irony GenerationSep 13 2019Ironies can not only express stronger emotions but also show a sense of humor. With the development of social media, ironies are widely used in public. Although many prior research studies have been conducted in irony detection, few studies focus on irony ... More
Neural Architectures for Fine-Grained Propaganda Detection in NewsSep 13 2019This paper describes our system (MIC-CIS) details and results of participation in the fine-grained propaganda detection shared task 2019. To address the tasks of sentence (SLC) and fragment level (FLC) propaganda detection, we explore different neural ... More
PubMedQA: A Dataset for Biomedical Research Question AnsweringSep 13 2019We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary ... More
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector SpacesSep 13 2019Distributional word vectors have recently been shown to encode many of the human biases, most notably gender and racial biases, and models for attenuating such biases have consequently been proposed. However, existing models and studies (1) operate on ... More
Neural Machine Translation with 4-Bit Precision and BeyondSep 13 2019Neural Machine Translation (NMT) is resource intensive. We design a quantization procedure to compress fit NMT models better for devices with limited hardware capability. We use logarithmic quantization, instead of the more commonly used fixed-point quantization, ... More
Towards Open-Domain Named Entity Recognition via Neural Correction ModelsSep 13 2019Named Entity Recognition (NER) plays an important role in a wide range of natural language processing tasks, such as relation extraction, question answering, etc. However, previous studies on NER are limited to a particular genre, using small manually-annotated ... More
Say What I Want: Towards the Dark Side of Neural Dialogue ModelsSep 13 2019Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations. However, there exists a dark side of these models -- due to the vulnerability of neural ... More
Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation ExtractionSep 13 2019Distant supervision (DS) has been widely used to automatically construct (noisy) labeled data for relation extraction (RE). Given two entities, distant supervision exploits sentences that directly mention them for predicting their semantic relation. We ... More
Sequence-to-sequence Pre-training with Data Augmentation for Sentence RewritingSep 13 2019We study sequence-to-sequence (seq2seq) pre-training with data augmentation for sentence rewriting. Instead of training a seq2seq model with gold training data and augmented data simultaneously, we separate them to train in different phases: pre-training ... More
End-to-End Neural Speaker Diarization with Permutation-Free ObjectivesSep 12 2019In this paper, we propose a novel end-to-end neural-network-based speaker diarization method. Unlike most existing methods, our proposed method does not have separate modules for extraction and clustering of speaker representations. Instead, our model ... More
Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using TwitterSep 12 2019Denial of Service (DoS) attacks are common in on-line and mobile services such as Twitter, Facebook and banking. As the scale and frequency of Distributed Denial of Service (DDoS) attacks increase, there is an urgent need for determining the impact of ... More
Analyzing machine-learned representations: A natural language case studySep 12 2019As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises of how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations ... More
The emotions that we perceive in music: the influence of language and lyrics comprehension on agreementSep 12 2019In the present study, we address the relationship between the emotions perceived in pop and rock music (mainly in Euro-American styles with English lyrics) and the language spoken by the listener. Our goal is to understand the influence of lyrics comprehension ... More
Finding Generalizable Evidence by Learning to Convince Q&A ModelsSep 12 2019We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. We train evidence agents to select the passage sentences that most convince a pretrained QA model ... More
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue DatasetSep 12 2019Virtual assistants such as Google Assistant, Alexa and Siri provide a conversational interface to a large number of services and APIs spanning multiple domains. Such systems need to support an ever-increasing number of services with possibly overlapping ... More
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERTSep 12 2019Transformer based architectures have become de-facto models used for a range of Natural Language Processing tasks. In particular, the BERT based models achieved significant accuracy gain for GLUE tasks, CoNLL-03 and SQuAD. However, BERT based models have ... More
Anonymising Queries by Semantic DecompositionSep 12 2019Protecting the privacy of search engine users is an important requirement in many information retrieval scenarios. A user might not want a search engine to guess his or her information need despite requesting relevant results. We propose a method to protect ... More
Self-Assembling Modular Networks for Interpretable Multi-Hop ReasoningSep 12 2019Multi-hop QA requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. The recently proposed HotpotQA (Yang et al., 2018) dataset is comprised of questions embodying four different multi-hop reasoning ... More