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DisMo: A Morphosyntactic, Disfluency and Multi-Word Unit Annotator. An Evaluation on a Corpus of French Spontaneous and Read SpeechFeb 08 2018We present DisMo, a multi-level annotator for spoken language corpora that integrates part-of-speech tagging with basic disfluency detection and annotation, and multi-word unit recognition. DisMo is a hybrid system that uses a combination of lexical resources, ... More
Praaline: Integrating Tools for Speech Corpus ResearchFeb 08 2018This paper presents Praaline, an open-source software system for managing, annotating, analysing and visualising speech corpora. Researchers working with speech corpora are often faced with multiple tools and formats, and they need to work with ever-increasing ... More
Biomedical term normalization of EHRs with UMLSFeb 08 2018This paper presents a novel prototype for biomedical term normalization of electronic health record excerpts with the Unified Medical Language System (UMLS) Metathesaurus. Despite being multilingual and cross-lingual by design, we first focus on processing ... More
Learning Inductive Biases with Simple Neural NetworksFeb 08 2018People use rich prior knowledge about the world in order to efficiently learn new concepts. These priors - also known as "inductive biases" - pertain to the space of internal models considered by a learner, and they help the learner make inferences that ... More
Joint Modeling of Accents and Acoustics for Multi-Accent Speech RecognitionFeb 07 2018The performance of automatic speech recognition systems degrades with increasing mismatch between the training and testing scenarios. Differences in speaker accents are a significant source of such mismatch. The traditional approach to deal with multiple ... More
Enhance word representation for out-of-vocabulary on Ubuntu dialogue corpusFeb 07 2018Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is the large number of out-of-vocabulary ... More
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context ModelingFeb 07 2018Automatic speech recognition (ASR) systems lack joint optimization during decoding over the acoustic, lexical and language models; for instance the ASR will often prune words due to acoustics using short-term context, prior to rescoring with long-term ... More
Unsupervised word sense disambiguation in dynamic semantic spacesFeb 07 2018In this paper, we are mainly concerned with the ability to quickly and automatically distinguish word senses in dynamic semantic spaces in which new terms and new senses appear frequently. Such spaces are built '"on the fly" from constantly evolving data ... More
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep LearningFeb 07 2018Privacy policies are the primary channel through which companies inform users about their data collection and sharing practices. In their current form, policies remain long and difficult to comprehend, thus merely serving the goal of legally protecting ... More
Semi-Amortized Variational AutoencodersFeb 07 2018Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network. While AVI has enabled efficient training of deep generative models such as variational autoencoders (VAE), recent empirical work suggests ... More
An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical NotesFeb 07 2018Code assignment is important on many levels in the modern hospital, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious, subjective, and requires medical coders with extensive ... More
Efficient Large-Scale Multi-Modal ClassificationFeb 06 2018While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal. Here, we examine multi-modal classification where one modality is discrete, e.g. text, and the other is continuous, e.g. visual representations ... More
How to Make Causal Inferences Using TextsFeb 06 2018New text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories of interest from large collections of text. We introduce a conceptual framework for making causal inferences ... More
Non-Projective Dependency Parsing via Latent Heads Representation (LHR)Feb 06 2018In this paper, we introduce a novel approach based on a bidirectional recurrent autoencoder to perform globally optimized non-projective dependency parsing via semi-supervised learning. The syntactic analysis is completed at the end of the neural process ... More
Investigations on Knowledge Base Embedding for Relation Prediction and ExtractionFeb 06 2018We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ... More
Système de traduction automatique statistique Anglais-ArabeFeb 06 2018Machine translation (MT) is the process of translating text written in a source language into text in a target language. In this article, we present our English-Arabic statistical machine translation system. First, we present the general process for setting ... More
Improving Variational Encoder-Decoders in Dialogue GenerationFeb 06 2018Variational encoder-decoders (VEDs) have shown promising results in dialogue generation. However, the latent variable distributions are usually approximated by a much simpler model than the powerful RNN structure used for encoding and decoding, yielding ... More
Texygen: A Benchmarking Platform for Text Generation ModelsFeb 06 2018We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality ... More
Decoding-History-Based Adaptive Control of Attention for Neural Machine TranslationFeb 06 2018Attention-based sequence-to-sequence model has proved successful in Neural Machine Translation (NMT). However, the attention without consideration of decoding history, which includes the past information in the decoder and the attention mechanism, often ... More
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified TextFeb 05 2018Feb 06 2018Existing text generation methods tend to produce repeated and "boring" expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward ... More
Order matters: Distributional properties of speech to young children bootstraps learning of semantic representationsFeb 02 2018Some researchers claim that language acquisition is critically dependent on experiencing linguistic input in order of increasing complexity. We set out to test this hypothesis using a simple recurrent neural network (SRN) trained to predict word sequences ... More
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to CroatianFeb 02 2018This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement ... More
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge GraphJan 31 2018While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been studied independently, ... More
The New Modality: Emoji Challenges in Prediction, Anticipation, and RetrievalJan 30 2018Feb 02 2018Over the past decade, emoji have emerged as a new and widespread form of digital communication, spanning diverse social networks and spoken languages. We propose to treat these ideograms as a new modality in their own right, distinct in their semantic ... More
Using Additional Indexes for Fast Full-Text Search of Phrases That Contains Frequently Used WordsJan 27 2018Searches for phrases and word sets in large text arrays by means of additional indexes are considered. Their use may reduce the query-processing time by an order of magnitude in comparison with standard inverted files.
Combining Convolution and Recursive Neural Networks for Sentiment AnalysisJan 27 2018This paper addresses the problem of sentence-level sentiment analysis. In recent years, Convolution and Recursive Neural Networks have been proven to be effective network architecture for sentence-level sentiment analysis. Nevertheless, each of them has ... More
Unsupervised Open Relation ExtractionJan 22 2018We explore methods to extract relations between named entities from free text in an unsupervised setting. In addition to standard feature extraction, we develop a novel method to re-weight word embeddings. We alleviate the problem of features sparsity ... More
BiographyNet: Extracting Relations Between People and EventsJan 22 2018This paper describes BiographyNet, a digital humanities project (2012-2016) that brings together researchers from history, computational linguistics and computer science. The project uses data from the Biography Portal of the Netherlands (BPN), which ... More
A Deep Reinforcement Learning Chatbot (Short Version)Jan 20 2018We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both ... More
What Level of Quality can Neural Machine Translation Attain on Literary Text?Jan 15 2018Given the rise of a new approach to MT, Neural MT (NMT), and its promising performance on different text types, we assess the translation quality it can attain on what is perceived to be the greatest challenge for MT: literary text. Specifically, we target ... More
Analysis of Wikipedia-based Corpora for Question AnsweringJan 06 2018Feb 05 2018This paper gives comprehensive analyses of corpora based on Wikipedia for several tasks in question answering. Four recent corpora are collected,WikiQA, SelQA, SQuAD, and InfoQA, and first analyzed intrinsically by contextual similarities, question types, ... More
Learning Continuous User Representations through Hybrid Filtering with doc2vecDec 31 2017Players in the online ad ecosystem are struggling to acquire the user data required for precise targeting. Audience look-alike modeling has the potential to alleviate this issue, but models' performance strongly depends on quantity and quality of available ... More
Subword and Crossword Units for CTC Acoustic ModelsDec 19 2017This paper proposes a novel approach to create an unit set for CTC based speech recognition systems. By using Byte Pair Encoding we learn an unit set of an arbitrary size on a given training text. In contrast to using characters or words as units this ... More
Social Emotion Mining Techniques for Facebook Posts Reaction PredictionDec 08 2017As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages ... More
Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural NetworksDec 05 2017We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward decomposition of the problem into entity detection, entity linking, relation ... More
Neural Cross-Lingual Entity LinkingDec 05 2017A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves ... More
One for All: Towards Language Independent Named Entity LinkingDec 05 2017Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc). Most previous EL research has focused mainly on one language, English, with less attention ... More
Visual Features for Context-Aware Speech RecognitionDec 01 2017Automatic transcriptions of consumer-generated multi-media content such as "Youtube" videos still exhibit high word error rates. Such data typically occupies a very broad domain, has been recorded in challenging conditions, with cheap hardware and a focus ... More
HoME: a Household Multimodal EnvironmentNov 29 2017We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts ... More
Can clone detection support quality assessments of requirements specifications?Nov 15 2017Due to their pivotal role in software engineering, considerable effort is spent on the quality assurance of software requirements specifications. As they are mainly described in natural language, relatively few means of automated quality assessment exist. ... More
Unsupervised patient representations from clinical notes with interpretable classification decisionsNov 14 2017We have two main contributions in this work: 1. We explore the usage of a stacked denoising autoencoder, and a paragraph vector model to learn task-independent dense patient representations directly from clinical notes. We evaluate these representations ... More
Interpretable probabilistic embeddings: bridging the gap between topic models and neural networksNov 11 2017We consider probabilistic topic models and more recent word embedding techniques from a perspective of learning hidden semantic representations. Inspired by a striking similarity of the two approaches, we merge them and learn probabilistic embeddings ... More
Deep Residual Learning for Small-Footprint Keyword SpottingOct 28 2017We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark. Our best residual network (ResNet) implementation significantly outperforms ... More
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram EmbeddingsOct 19 2017We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling replacement candidates and ranks them according to their semantic fit, by calculating ... More
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword SpottingOct 18 2017Nov 28 2017We describe Honk, an open-source PyTorch reimplementation of convolutional neural networks for keyword spotting that are included as examples in TensorFlow. These models are useful for recognizing "command triggers" in speech-based interfaces (e.g., "Hey ... More
Aligning Script Events with Narrative TextsOct 16 2017Script knowledge plays a central role in text understanding and is relevant for a variety of downstream tasks. In this paper, we consider two recent datasets which provide a rich and general representation of script events in terms of paraphrase sets. ... More
Annotating High-Level Structures of Short Stories and Personal AnecdotesOct 08 2017Stories are a vital form of communication in human culture; they are employed daily to persuade, to elicit sympathy, or to convey a message. Computational understanding of human narratives, especially high-level narrative structures, however, remain limited ... More
Language Independent Acquisition of AbbreviationsSep 23 2017This paper addresses automatic extraction of abbreviations (encompassing acronyms and initialisms) and corresponding long-form expansions from plain unstructured text. We create and are going to release a multilingual resource for abbreviations and their ... More
FiLM: Visual Reasoning with a General Conditioning LayerSep 22 2017Dec 18 2017We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We ... More
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]Sep 20 2017In knowledge bases such as Wikidata, it is possible to assert a large set of properties for entities, ranging from generic ones such as name and place of birth to highly profession-specific or background-specific ones such as doctoral advisor or medical ... More
SKOS Concepts and Natural Language Concepts: an Analysis of Latent Relationships in KOSsSep 16 2017The vehicle to represent Knowledge Organization Systems (KOSs) in the environment of the Semantic Web and linked data is the Simple Knowledge Organization System (SKOS). SKOS provides a way to assign a URI to each concept, and this URI functions as a ... More
Data Innovation for International Development: An overview of natural language processing for qualitative data analysisSep 16 2017Availability, collection and access to quantitative data, as well as its limitations, often make qualitative data the resource upon which development programs heavily rely. Both traditional interview data and social media analysis can provide rich contextual ... More
Weather impacts expressed sentimentAug 31 2017We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from ... More
A Simple LSTM model for Transition-based Dependency ParsingAug 29 2017Sep 08 2017We present a simple LSTM-based transition-based dependency parser. Our model is composed of a single LSTM hidden layer replacing the hidden layer in the usual feed-forward network architecture. We also propose a new initialization method that uses the ... More
CloudScan - A configuration-free invoice analysis system using recurrent neural networksAug 24 2017We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally ... More
Comparison of Decoding Strategies for CTC Acoustic ModelsAug 15 2017Connectionist Temporal Classification has recently attracted a lot of interest as it offers an elegant approach to building acoustic models (AMs) for speech recognition. The CTC loss function maps an input sequence of observable feature vectors to an ... More
The University of Edinburgh's Neural MT Systems for WMT17Aug 02 2017This paper describes the University of Edinburgh's submissions to the WMT17 shared news translation and biomedical translation tasks. We participated in 12 translation directions for news, translating between English and Czech, German, Latvian, Russian, ... More
Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question AnsweringJul 25 2017Most work on natural language question answering today focuses on answer selection: given a candidate list of sentences, determine which contains the answer. Although important, answer selection is only one stage in a standard end-to-end question answering ... More
Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media StreamsJul 25 2017Time is an important relevance signal when searching streams of social media posts. The distribution of document timestamps from the results of an initial query can be leveraged to infer the distribution of relevant documents, which can then be used to ... More
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space SpecialisationJul 21 2017Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets ... More
End-to-End Information Extraction without Token-Level SupervisionJul 16 2017Most state-of-the-art information extraction approaches rely on token-level labels to find the areas of interest in text. Unfortunately, these labels are time-consuming and costly to create, and consequently, not available for many real-life IE tasks. ... More
Multitask Learning for Fine-Grained Twitter Sentiment AnalysisJul 12 2017Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately. We argue that such classification tasks are correlated and we propose a multitask approach ... More
Learning Visual Reasoning Without Strong PriorsJul 10 2017Dec 18 2017Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, ... More
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation ProjectionJul 08 2017The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and ... More
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type MappingJul 08 2017The state-of-the-art named entity recognition (NER) systems are statistical machine learning models that have strong generalization capability (i.e., can recognize unseen entities that do not appear in training data) based on lexical and contextual information. ... More
Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological ReinflectionJul 05 2017Jul 06 2017This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the inflected form given a lemma and a set of morpho-syntactic features. We focus on neural network ... More
Improving Slot Filling Performance with Attentive Neural Networks on Dependency StructuresJul 04 2017Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities\_of\_residence) for a given entity from a large collection of source documents. In this paper we propose an effective DNN architecture for SF ... More
Modulating early visual processing by languageJul 02 2017Dec 18 2017It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in computational models for language-vision tasks, where visual and linguistic input ... More
Grounded Language Learning in a Simulated 3D WorldJun 20 2017Jun 26 2017We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language ... More
CRNN: A Joint Neural Network for Redundancy DetectionJun 04 2017This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character-aware convolutional neural network (Char-CNN) with character-aware recurrent ... More
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific RulesJun 01 2017Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional ... More
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual ConstraintsJun 01 2017We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically ... More
Dynamics of core of language vocabularyMay 29 2017Studies of the overall structure of vocabulary and its dynamics became possible due to creation of diachronic text corpora, especially Google Books Ngram. This article discusses the question of core change rate and the degree to which the core words cover ... More
Mixed Membership Word Embeddings for Computational Social ScienceMay 20 2017May 25 2017Word embeddings improve the performance of NLP systems by revealing the hidden structural relationships between words. These models have recently risen in popularity due to the performance of scalable algorithms trained in the big data setting. Despite ... More
Decoding Sentiment from Distributed Representations of SentencesMay 17 2017Jun 16 2017Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, ... More
Using Titles vs. Full-text as Source for Automated Semantic Document AnnotationMay 15 2017Sep 27 2017A significant part of the largest Knowledge Graph today, the Linked Open Data cloud, consists of metadata about documents such as publications, news reports, and other media articles. While the widespread access to the document metadata is a tremendous ... More
Query-adaptive Video Summarization via Quality-aware Relevance EstimationMay 01 2017Sep 28 2017Although the problem of automatic video summarization has recently received a lot of attention, the problem of creating a video summary that also highlights elements relevant to a search query has been less studied. We address this problem by posing query-relevant ... More
Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine TranslationApr 14 2017Neural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT). Despite its recent success, NMT cannot handle a larger ... More
Neural Machine Translation Model with a Large Vocabulary Selected by Branching EntropyApr 14 2017Sep 06 2017Neural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT). Despite its recent success, NMT cannot handle a larger ... More
Cardinal Virtues: Extracting Relation Cardinalities from TextApr 14 2017May 27 2017Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap ... More
A Short Review of Ethical Challenges in Clinical Natural Language ProcessingMar 29 2017Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused ... More
Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based ArchitectureMar 17 2017Oct 05 2017In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is used to extract ... More
End-to-end optimization of goal-driven and visually grounded dialogue systemsMar 15 2017End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as ... More
InScript: Narrative texts annotated with script informationMar 15 2017This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. ... More
Reinforcement Learning for Transition-Based Mention DetectionMar 13 2017This paper describes an application of reinforcement learning to the mention detection task. We define a novel action-based formulation for the mention detection task, in which a model can flexibly revise past labeling decisions by grouping together tokens ... More
Massive Exploration of Neural Machine Translation ArchitecturesMar 11 2017Mar 21 2017Neural Machine Translation (NMT) has shown remarkable progress over the past few years with production systems now being deployed to end-users. One major drawback of current architectures is that they are expensive to train, typically requiring days to ... More
The cognitive roots of regularization in languageMar 09 2017Regularization occurs when the output a learner produces is less variable than the linguistic data they observed. In an artificial language learning experiment, we show that there exist at least two independent sources of regularization bias in cognition: ... More
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment ClassificationMar 07 2017This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically ... More
On the Relevance of Auditory-Based Gabor Features for Deep Learning in Automatic Speech RecognitionFeb 14 2017Previous studies support the idea of merging auditory-based Gabor features with deep learning architectures to achieve robust automatic speech recognition, however, the cause behind the gain of such combination is still unknown. We believe these representations ... More
Bilateral Multi-Perspective Matching for Natural Language SentencesFeb 13 2017Jul 14 2017Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose ... More
Multi-level computational methods for interdisciplinary research in the HathiTrust Digital LibraryFeb 03 2017Jun 08 2017We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. ... More
Analysing Temporal Evolution of Interlingual Wikipedia Article PairsFeb 02 2017Wikipedia articles representing an entity or a topic in different language editions evolve independently within the scope of the language-specific user communities. This can lead to different points of views reflected in the articles, as well as complementary ... More
Single-Pass, Adaptive Natural Language Filtering: Measuring Value in User Generated Comments on Large-Scale, Social Media News ForumsJan 12 2017There are large amounts of insight and social discovery potential in mining crowd-sourced comments left on popular news forums like,, and Hacker News. Unfortunately, due the overwhelming amount of participation with ... More
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence ModelsJan 11 2017Jul 31 2017Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response. Unlike translation, conversation responding is inherently creative. ... More
Verifying Heaps' law using Google Books Ngram dataDec 29 2016This article is devoted to the verification of the empirical Heaps law in European languages using Google Books Ngram corpus data. The connection between word distribution frequency and expected dependence of individual word number on text size is analysed ... More
Here's My Point: Joint Pointer Architecture for Argument MiningDec 28 2016May 08 2017One of the major goals in automated argumentation mining is to uncover the argument structure present in argumentative text. In order to determine this structure, one must understand how different individual components of the overall argument are linked. ... More
Multi-Perspective Context Matching for Machine ComprehensionDec 13 2016Previous machine comprehension (MC) datasets are either too small to train end-to-end deep learning models, or not difficult enough to evaluate the ability of current MC techniques. The newly released SQuAD dataset alleviates these limitations, and gives ... More
Unraveling reported dreams with text analyticsDec 12 2016We investigate what distinguishes reported dreams from other personal narratives. The continuity hypothesis, stemming from psychological dream analysis work, states that most dreams refer to a person's daily life and personal concerns, similar to other ... More
#HashtagWars: Learning a Sense of HumorDec 09 2016Apr 15 2017In this work, we present a new dataset for computational humor, specifically comparative humor ranking, which attempts to eschew the ubiquitous binary approach to humor detection. The dataset consists of tweets that are humorous responses to a given hashtag. ... More
Evaluating Creative Language Generation: The Case of Rap Lyric GhostwritingDec 09 2016Language generation tasks that seek to mimic human ability to use language creatively are difficult to evaluate, since one must consider creativity, style, and other non-trivial aspects of the generated text. The goal of this paper is to develop evaluation ... More