Articles

This paper investigates the effects of data size and frequency range on distributional seman- tic models. We compare the performance of a number of representative models for several test settings over data of varying sizes, and over test items of various frequency. Our re- sults show that neural network-based models …...

We investigate mutual benefits between syn- tax and semantic roles using neural network models, by studying a parsing→SRL pipeline, a SRL→parsing pipeline, and a simple joint model by embedding sharing. The integra- tion of syntactic and semantic features gives promising results in a Chinese Semantic Tree- bank, demonstrating large potentials …...

Canonical morphological segmentation aims to divide words into a sequence of stan- dardized segments. In this work, we propose a character-based neural encoder- decoder model for this task. Additionally, we extend our model to include morpheme- level and lexical information through a neural reranker. We set the new state of …...

In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alle- viate issues of repeating and dropping trans- lations in NMT. For each source word, our model starts with a full coverage embedding vector to track the coverage status, and then keeps …...

This paper considers the problem of learning Chinese word embeddings. In contrast to En- glish, a Chinese word is usually composed of characters, and most of the characters them- selves can be further divided into components such as radicals. While characters and radical- s contain rich information and are capable …...

In this work we present a generalisation of the Modified Kneser-Ney interpolative smoothing for richer smoothing via additional discount parameters. We provide mathematical under- pinning for the estimator of the new discount parameters, and showcase the utility of our rich MKN language models on several Euro- pean languages. We further …...

In this paper, we explore the possibility of leveraging Residual Networks (ResNet), a powerful structure in constructing extremely deep neural network for image understanding, to improve recurrent neural networks (RNN) for modeling sequential data. We show that for sequence classification tasks, incorporat- ing residual connections into recurrent struc- tures yields …...

We conduct large-scale studies on ‘human at- tention’ in Visual Question Answering (VQA) to understand where humans choose to look to answer questions about images. We de- sign and test multiple game-inspired novel attention-annotation interfaces that require the subject to sharpen regions of a blurred im- age to answer a …...

Temporal common sense has applications in AI tasks such as QA, multi-document summa- rization, and human-AI communication. We propose the task of sequencing – given a jum- bled set of aligned image-caption pairs that belong to a story, the task is to sort them such that the output sequence forms …...

We propose a domain adaptation framework, and formally prove that it generalizes the fea- ture augmentation technique in (Daum´e III, 2007) and the multi-task regularization frame- work in (Evgeniou and Pontil, 2004). We show that our framework is strictly more gen- eral than these approaches and allows practi- tioners to …...

Visual Question Answering (VQA) is the task of answering natural-language questions about images. We introduce the novel prob- lem of determining the relevance of questions to images in VQA. Current VQA models do not reason about whether a question is even related to the given image (e.g., What is the …...

An exciting outcome of research at the inter- section of language and vision is that of zero- shot learning (ZSL). ZSL promises to scale visual recognition by borrowing distributed semantic models learned from linguistic cor- pora and turning them into visual recognition models. However the popular word-vector DSM embeddings are …...

Convolutional neural networks (CNN) have achieved the top performance for event de- tection due to their capacity to induce the underlying structures of the k-grams in the sentences. However, the current CNN-based event detectors only model the consecutive k-grams and ignore the non-consecutive k- grams that might involve important structures …...

In named entity recognition, we often don’t have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we pro- pose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types …...

Many downstream NLP tasks can benefit from Open Information Extraction (Open IE) as a semantic representation. While Open IE sys- tems are available for English, many other languages lack such tools. In this paper, we present a straightforward approach for adapt- ing PropS, a rule-based predicate-argument analysis for English, to …...

Stance detection is the task of classifying the attitude Previous work has assumed that ei- ther the target is mentioned in the text or that training data for every target is given. This pa- per considers the more challenging version of this task, where targets are not always men- tioned …...

Chinese character riddle is a riddle game in which the riddle solution is a single Chi- nese character. It is closely connected with the shape, pronunciation or meaning of Chi- nese characters. The riddle description (sen- tence) is usually composed of phrases with rich linguistic phenomena (such as pun, sim- …...

Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In the clinical domain one major ap- plication of sequence labeling involves ex- traction of relevant entities such as medica- tion, indication, and side-effects from Elec- tronic Health Record Narratives. Sequence …...

We present our pilot research on automatically extracting subevents from a domain-specific corpus, focusing on the type of subevents that describe physical actions composing an event. We decompose the challenging problem and propose a two-phase approach that effectively captures sentential and local cues that describe subevents. We extracted a rich …...

Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. The task of automatically extracting them have received considerable attention in recent decades. Different from previous studies, which are usually focused on automatically extracting keyphrases from documents or articles, in this study, we …...