Articles

Language documentation begins by gather- ing speech. Manual or automatic transcrip- tion at the word level is typically not possi- ble because of the absence of an orthography or prior lexicon, and though manual phone- mic transcription is possible, it is prohibitively slow. On the other hand, translations of the …...

We introduce the first global recursive neural parsing model with optimality guarantees dur- ing decoding. To support global features, we give up dynamic programs and instead search directly in the space of all possible subtrees. Although this space is exponentially large in the sentence length, we show it is possible …...

This paper presents a generic approach to con- tent selection for creating timelines from in- dividual history articles for which no exter- nal information about the same topic is avail- able. This scenario is in contrast to existing works on timeline generation, which require the presence of a large corpus …...

Most existing knowledge base (KB) embed- ding methods solely learn from time-unknown fact triples but neglect the temporal informa- tion in the knowledge base. In this paper, we propose a novel time-aware KB embed- ding approach taking advantage of the hap- pening time of facts. Specifically, we use tem- poral …...

This paper demonstrates that it is possible for a parser to improve its performance with a hu- man in the loop, by posing simple questions to non-experts. For example, given the first sentence of this abstract, if the parser is un- certain about the subject of the verb “pose,” it …...

We recast syntactic parsing as a language modeling problem and use recent advances in neural network language modeling to achieve a new state of the art for constituency Penn Treebank parsing — 93.8 F1 on section 23, us- ing 2-21 as training, 24 as development, plus tri-training. When trees are …...

Morphological segmentation has traditionally been modeled with non-hierarchical models, which yield flat segmentations as output. In many cases, however, proper morphologi- cal analysis requires hierarchical structure— especially in the case of derivational morphol- ogy. In this work, we introduce a discrimina- tive, joint model of morphological segmenta- tion along with …...

Recent work on word ordering has argued that syntactic structure is important, or even re- quired, for effectively recovering the order of a sentence. We find that, in fact, an n-gram language model with a simple heuristic gives strong results on this task. Furthermore, we show that a long short-term …...

Most successful information extraction sys- tems operate with access to a large collec- tion of documents. In this work, we explore the task of acquiring and incorporating exter- nal evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, …...

We introduce a novel approach to the decoding problem in transition-based parsing: heuris- tic backtracking. This algorithm uses a se- ries of partial parses on the sentence to locate the best candidate parse, using confidence es- timates of transition decisions as a heuristic to guide the starting points of the …...

To alleviate the shortage of labeled data, we propose to use bilingually-constrained syn- thetic implicit data for implicit discourse re- lation recognition. These data are extracted from a bilingual sentence-aligned corpus ac- cording to the implicit/explicit mismatch be- tween different languages. Incorporating these data via a multi-task neural network model …...

Open information extraction (Open IE) was presented as an unrestricted variant of tra- ditional information extraction. It has been gaining substantial attention, manifested by a large number of automatic Open IE extractors and downstream applications. In spite of this broad attention, the Open IE task definition has been lacking – …...

In this paper, we improve the attention or alignment accuracy of neural machine trans- lation by utilizing the alignments of train- ing sentence pairs. We simply compute the distance between the machine attentions and the “true” alignments, and minimize this cost in the training procedure. Our experi- ments on large-scale …...

We investigate how neural, encoder-decoder translation systems output target strings of ap- propriate lengths, finding that a collection of hidden units learns to explicitly implement this functionality. 1...

Coreference resolution systems are typically trained with heuristic loss functions that re- quire careful tuning. In this paper we in- stead apply reinforcement learning to directly optimize a neural mention-ranking model for coreference evaluation metrics. We experi- ment with two approaches: the REINFORCE policy gradient algorithm and a reward- rescaled …...

Discourse parsing is considered as one of the most challenging natural language processing (NLP) tasks. Implicit discourse relation clas- sification is the bottleneck for discourse pars- ing. Without the guide of explicit discourse connectives, the relation of sentence pairs are very hard to be inferred. This paper proposes a stacking …...

We propose a simple neural architecture for nat- ural language inference. Our approach uses at- tention to decompose the problem into subprob- lems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natu- ral Language Inference (SNLI) dataset, we ob- tain state-of-the-art results with almost an …...

In this paper we discuss a process for quan- tifying the behavioral impact of a domain- customized machine translation system de- ployed on a large-scale e-commerce platform. We discuss several machine translation sys- tems that we trained using aligned text from product listing descriptions written in mul- tiple languages. We …...

The production of color language is essential for grounded language generation. Color de- scriptions have many challenging properties: they can be vague, compositionally complex, and denotationally rich. We present an effec- tive approach to generating color descriptions using recurrent neural networks and a Fourier- transformed color representation. Our model outperforms …...

This paper presents a system that compo- sitionally maps outputs of a wide-coverage Japanese CCG parser onto semantic represen- tations and performs automated inference in higher-order logic. The system is evaluated on a textual entailment dataset. It is shown that the system solves inference problems that focus on a variety …...