Articles

Dependency tree-to-tree translation models are powerful because they can naturally han- dle long range reorderings which is important for distant language pairs. The translation pro- cess is easy if it can be accomplished only by replacing non-terminals in translation rules with other rules. However it is sometimes necessary to adjoin …...

We have constructed a new “Who-did-What” dataset of over 200,000 fill-in-the-gap (cloze) multiple choice reading comprehension prob- lems constructed from the LDC English Gi- gaword newswire corpus. The WDW dataset has a variety of novel features. First, in con- trast with the CNN and Daily Mail datasets (Hermann et al., …...

We present a study on two key character- istics of human syntactic annotations: an- choring and agreement. Anchoring is a well known cognitive bias in human decision mak- ing, where judgments are drawn towards pre- existing values. We study the influence of anchoring on a standard approach to creation of …...

We study the problem of jointly aligning sen- tence constituents and predicting their sim- ilarities. While extensive sentence similar- ity data exists, manually generating reference alignments and labeling the similarities of the aligned chunks is comparatively onerous. This prompts the natural question of whether we can exploit easy-to-create sentence level …...

Mapping word embeddings of different lan- guages into a single space has multiple appli- cations. In order to map from a source space into a target space, a common approach is to learn a linear mapping that minimizes the dis- tances between equivalences listed in a bilin- gual dictionary. In …...

Recently a variety of LSTM-based condi- tional language models (LM) have been ap- plied across a range of language generation tasks. In this work we study various model ar- chitectures and different ways to represent and aggregate the source information in an end- to-end neural dialogue system framework. A method …...

In this paper, we propose a new generative ap- proach for semantic slot filling task in spoken language understanding using a nonparamet- ric Bayesian formalism. Slot filling is typi- cally formulated as a sequential labeling prob- lem, which does not directly deal with the pos- terior distribution of possible slot …...

We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the for- ward and backward parsing directions. The parsing procedure for each direction is for- mulated as sequentially querying the memory component that stores continuous headword embeddings. The proposed parser makes use …...

To create conversational systems working in actual situations, it is crucial to assume that they interact with multiple agents. In this work, we tackle addressee and response se- lection for multi-party conversation, in which systems are expected to select whom they ad- dress as well as what they say. The …...

Verbs play a critical role in the meaning of sentences, but these ubiquitous words have re- ceived little attention in recent distributional se- mantics research. We introduce SimVerb-3500, an evaluation resource that provides human ratings for the similarity of 3,500 verb pairs. SimVerb-3500 covers all normed verb types from the …...

Words shift in meaning for many reasons, including cultural factors like new technolo- gies and regular linguistic processes like sub- jectification. Understanding the evolution of language and culture requires disentangling these underlying causes. Here we show how two different distributional measures can be used to detect two different types of …...

Current methods for automatically evaluating grammatical error correction (GEC) systems rely on gold-standard references. However, these methods suffer from penalizing gram- matical edits that are correct but not in the gold standard. We show that reference-less grammaticality metrics correlate very strongly with human judgments and are competitive with the leading …...

Language resources that systematically orga- nize paraphrases for binary relations are of great value for various NLP tasks and have re- cently been advanced in projects like PATTY, WiseNet and DEFIE. This paper presents a new method for building such a resource and the resource itself, called POLY. Starting with …...

We propose a text-based recommendation en- gine that utilizes recurrent neural networks to flexibly map textual input into continuous vec- tor representations tailored to the recommen- dation task. Here, the text objects are doc- uments such as Wikipedia articles or ques- tion and answer pairs. As neural models re- quire …...

This study measures the effects of Federal Open Market Committee text content on the direction of short- and medium-term interest rate movements. Because the words relevant to short- and medium-term interest rates differ, we apply a supervised approach to learn dis- tinct sets of topics for each dependent variable being …...

We consider the task of predicting lexical entailment using distributional vectors. We perform a novel qualitative analysis of one existing model which was previously shown to only measure the prototypicality of word pairs. We find that the model strongly learns to identify hypernyms using Hearst patterns, which are well known …...

We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not avail- able. Recent works in response generation have adopted metrics from machine transla- tion to compare a model’s generated response to a single target response. We show that these metrics correlate very …...

The task of AMR-to-text generation is to gen- erate grammatical text that sustains the seman- tic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR graph into smaller fragments, and then gener- ating the translation for each fragment, before finally deciding the order …...

Recurrent Neural Network (RNN) and one of its specific architectures, Long Short-Term Memory (LSTM), have been widely used for sequence labeling. Explicitly modeling out- put label dependencies on top of RNN/LSTM is a widely-studied and effective extension. We propose another extension to incorpo- rate the global information spanning over the …...

In this paper, we experiment with a re- source consisting of metaphorically annotated proverbs on the task of word-level metaphor recognition. We observe that existing feature sets do not perform well on this data. We design a novel set of features to better cap- ture the peculiar nature of proverbs …...