Articles

Neural machine translation (NMT) offers a novel alternative formulation of translation that is potentially simpler than statistical ap- proaches. However to reach competitive per- formance, NMT models need to be exceed- ingly large. In this paper we consider applying knowledge distillation approaches (Bucila et al., 2006; Hinton et al., 2015) …...

Deep inference on a large-scale knowledge base (KB) needs a mass of formulas, but it is almost impossible to create all formulas man- ually. Data-driven methods have been pro- posed to mine formulas from KBs automat- ically, where random sampling and approx- imate calculation are common techniques to handle big …...

The state-of-the-art named entity recognition (NER) systems are statistical machine learn- ing models that have strong generalization ca- pability (i.e., can recognize unseen entities that do not appear in training data) based on lexical and contextual information. How- ever, such a model could still make mis- takes if its features …...

We introduce an online neural sequence to se- quence model that learns to alternate between encoding and decoding segments of the input as it is read. By independently tracking the en- coding and decoding representations our algo- rithm permits exact polynomial marginaliza- tion of the latent segmentation during train- ing, …...

Sequence-to-Sequence (seq2seq) modeling has rapidly become an important general- purpose NLP tool that has proven effective for many text-generation and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next-word distributions. In this work, we introduce a model and beam- search training …...

Crosslingual word embeddings represent lexi- cal items from different languages in the same vector space, enabling transfer of NLP tools. However, previous attempts had expensive re- source requirements, difficulty incorporating monolingual data or were unable to handle polysemy. We address these drawbacks in our method which takes advantage of a …...

For many low-resource languages, spoken lan- guage resources are more likely to be an- notated with translations than with transcrip- tions. Translated speech data is potentially valuable for documenting endangered lan- guages or for training speech translation sys- tems. A first step towards making use of such data would be …...

This paper proposes a method for intra- sentential subject zero anaphora resolution in Japanese. Our proposed method utilizes a Multi-column Convolutional Neural Network (MCNN) for predicting zero anaphoric rela- tions. Motivated by Centering Theory and other previous works, we exploit as clues both the surface word sequence and the dependency …...

Recognizing implicit discourse relations is a challenging but important task in the field of Natural Language Processing. For such a complex text processing task, different from previous studies, we argue that it is necessary to repeatedly read the arguments and dynamically exploit the efficient features useful for recognizing discourse relations. …...

This article tackles a new challenging task in computational argumentation. Given a pair of two arguments to a certain controversial topic, we aim to directly assess qualitative properties of the arguments in order to explain why one argument is more convincing than the other one. We approach this task in …...

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be short- sighted, predicting utterances one at a time while ignoring their influence on future out- comes. Modeling the future direction of a di- alogue is crucial to generating coherent, inter- esting …...

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. It generates biographical sen- tences from fact tables on a new dataset of biographies from Wikipedia. This set is an order of magnitude larger than existing re- sources with over 700k samples and a 400k …...

Human evaluation of machine translation nor- mally uses sentence-level measures such as relative ranking or adequacy scales. However, these provide no insight into possible errors, and do not scale well with sentence length. We argue for a semantics-based evaluation, which captures what meaning components are retained in the MT output, …...

We describe Hafez, a program that gener- ates any number of distinct poems on a user- supplied topic. rhyme constraints.Poems obey rhythmic and We describe the poetry- generation algorithm, give experimental data concerning its parameters, and show its gener- ality with respect to language and poetic form. 1...

We present a model for contrastively describ- ing scenes, in which context-specific behav- ior results from a combination of inference- driven pragmatics and learned semantics. Like previous learned approaches to language gen- eration, our model uses a simple feature- driven architecture (here a pair of neural “lis- tener” and “speaker” …...

Sluicing is an elliptical process where the ma- jority of a question can go unpronounced as long as there is a salient antecedent in previ- ous discourse. This paper considers the task of antecedent selection: finding the correct antecedent for a given case of sluicing. We argue that both syntactic …...

Language models (LMs) are statistical mod- els that calculate probabilities over sequences of words or other discrete symbols. Currently two major paradigms for language model- ing exist: count-based n-gram models, which have advantages of scalability and test-time speed, and neural LMs, which often achieve superior modeling performance. We demon- strate …...

Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision tasks. Their application to language has received much less attention, and it has mainly focused on static classifica- tion tasks, such as sentence classification for Sentiment Analysis or relation extraction. In this work, we study …...

Linguistic research on multilingual societies has indicated that there is usually a preferred language for expression of emotion and sen- timent (Dewaele, 2010). Paucity of data has limited such studies to participant interviews and speech transcriptions from small groups of speakers. In this paper, we report a study on 430,000 …...

Significant events are characterized by interac- tions between entities (such as countries, or- ganizations, or individuals) that deviate from typical interaction patterns. Analysts, includ- ing historians, political scientists, and journal- ists, commonly read large quantities of text to construct an accurate picture of when and where an event happened, who …...