Articles

We introduce a simple semi-supervised ap- proach to improve implicit discourse relation identification. This approach harnesses large amounts of automatically extracted discourse connectives along with their arguments to con- struct new distributional word representations. Specifically, we represent words in the space of discourse connectives as a way to directly encode …...

Embedding knowledge graphs into continuous vector spaces has recently attracted increasing interest. Most existing methods perform the embedding task using only fact triples. Logi- cal rules, although containing rich background information, have not been well studied in this task. This paper proposes a novel method of jointly embedding knowledge graphs …...

A common problem in cognitive modelling is lack of access to accurate broad-coverage models of event-level surprisal. As shown in, e.g., Bicknell et al. (2010), event-level knowl- edge does affect human expectations for ver- bal arguments. For example, the model should be able to predict that mechanics are likely to …...

Word embedding has been widely studied and proven helpful in solving many natural lan- guage processing tasks. However, the ambi- guity of natural language is always a prob- lem on learning high quality word embed- dings. A possible solution is sense embed- ding which trains embedding for each sense of …...

In this work, we show the possibility of infer- ring the answer type before solving a factoid question and leveraging the type information to improve semantic parsing. By replacing the topic entity in a question with its type, we are able to generate an abstract form of the ques- tion, …...

A common model for question answering (QA) is that a good answer is one that is closely related to the question, where re- latedness is often determined using general- purpose lexical models such as word embed- dings. We argue that a better approach is to look for answers that are …...

We present EpiReader, a novel model for ma- chine comprehension of text. Machine com- prehension of unstructured, real-world text is a major research goal for natural language pro- cessing. Current tests of machine comprehen- sion pose questions whose answers can be in- ferred from some supporting text, and evaluate a …...

Neural network based models have achieved impressive results on various specific tasks. However, in previous works, most models are learned separately based on single-task su- pervised objectives, which often suffer from insufficient training data. In this paper, we propose two deep architectures which can be trained jointly on multiple related …...

Prediction without justification has limited ap- plicability. As a remedy, we learn to extract pieces of input text as justifications – ratio- nales – that are tailored to be short and co- herent, yet sufficient for making the same pre- diction. Our approach combines two modu- lar components, generator and …...

When humans read text, they fixate some words and skip others. However, there have been few attempts to explain skipping behav- ior with computational models, as most ex- isting work has focused on predicting read- ing times (e.g., using surprisal). In this pa- per, we propose a novel approach that …...

Natural language acquisition relies on appro- priate generalization: the ability to produce novel sentences, while learning to restrict pro- ductions to acceptable forms in the language. Psycholinguists have proposed various prop- erties that might play a role in guiding appro- priate generalizations, looking at learning of verb alternations as a …...

Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using background knowledge to select the correct answer. We present Parsing to Probabilistic Programs (P 3), a novel situ- ated question answering …...

This paper focuses on the study of recognizing discontiguous entities. Motivated by a previ- ous work, we propose to use a novel hyper- graph representation to jointly encode discon- tiguous entities of unbounded length, which can overlap with one another. To compare with existing approaches, we first formally in- troduce …...

With the renaissance of neural network in re- cent years, relation classification has again become a research hotspot in natural lan- guage processing, and leveraging parse trees is a common and effective method of tack- ling this problem. In this work, we offer a new perspective on utilizing syntactic infor- …...

The challenges of Machine Reading and Knowledge Extraction at a web scale re- quire a system capable of extracting diverse information from large, heterogeneous cor- pora. The Open Information Extraction (OIE) paradigm aims at extracting assertions from large corpora without requiring a vocabulary or relation-specific training data. Most sys- tems …...

Determining whether a major societal event has already happened, is still on-going, or may occur in the future is crucial for event pre- diction, timeline generation, and news sum- marization. We introduce a new task and a new corpus, EventStatus, which has 4500 En- glish and Spanish articles about civil …...

Center-embedding is difficult to process and is known as a rare syntactic construction across languages. In this paper we describe a method to incorporate this assumption into the gram- mar induction tasks by restricting the search space of a model to trees with limited center- embedding. The key idea is …...

We propose a neural-network based model for coordination boundary prediction. The net- work is designed to incorporate two signals: the similarity between conjuncts and the ob- servation that replacing the whole coordina- tion phrase with a conjunct tends to produce a coherent sentences. The modeling makes use of several LSTM …...

Tree transducers that model expressive linguistic phenomena often require word- alignments and a heuristic rule extractor to induce their grammars. However, when the corpus of tree/string pairs is small compared to the size of the vocabulary or the com- plexity of the grammar, word-alignments are unreliable. We propose a general …...

Parsing accuracy using efficient greedy transi- tion systems has improved dramatically in re- cent years thanks to neural networks. Despite striking results in dependency parsing, how- ever, neural models have not surpassed state- of-the-art approaches in constituency parsing. To remedy this, we introduce a new shift- reduce system whose stack …...