Articles

This paper presents a novel approach to improve reordering in phrase-based ma- chine translation by using richer, syntac- tic representations of units of bilingual language models (BiLMs). Our method to include syntactic information is simple in implementation and requires minimal changes in the decoding algorithm. The approach is evaluated in …...

Translating into morphologically rich lan- guages is a particularly difficult problem in machine translation due to the high de- gree of inflectional ambiguity in the tar- get language, often only poorly captured by existing word translation models. We present a general approach that exploits source-side contexts of foreign words to …...

To overcome the scarceness of bilingual corpora for some language pairs in ma- chine translation, pivot-based SMT uses pivot language as a "bridge" to generate source-target translation from source- pivot and pivot-target translation. One of the key issues is to estimate the probabili- ties for the generated phrase pairs. In …...

In this paper, we propose a novel neu- ral network model called RNN Encoder– Decoder that consists of two recurrent neural networks (RNN). One RNN en- codes a sequence of symbols into a fixed- length vector representation, and the other decodes the representation into another se- quence of symbols. The …...

Learning from errors is a crucial aspect of improving expertise. Based on this no- tion, we discuss a robust statistical frame- work for analysing the impact of different error types on machine translation (MT) output quality. Our approach is based on linear mixed-effects models, which allow the analysis of error-annotated …...

In this paper, we investigate a challeng- ing task of automatic related work gener- ation. Given multiple reference papers as input, the task aims to generate a related work section for a target paper. The gen- erated related work section can be used as a draft for the author to …...

Clustering aspect-related phrases in terms of product’s property is a precursor pro- cess to aspect-level sentiment analysis which is a central task in sentiment analy- sis. Most of existing methods for address- ing this problem are context-based models which assume that domain synonymous phrases share similar co-occurrence con- texts. In …...

When Part-of-Speech annotated data is scarce, e.g. for under-resourced lan- guages, one can turn to cross-lingual trans- fer and crawled dictionaries to collect par- tially supervised data. We cast this prob- lem in the framework of ambiguous learn- ing and show how to learn an accurate history-based model. Experiments on …...

There are several NLP systems whose ac- curacy depends crucially on finding mis- spellings fast. However, the classical ap- proach is based on a quadratic time algo- rithm with 80% coverage. We present a novel algorithm for misspelling detection, which runs in constant time and improves the coverage to more …...

Languages that have no explicit word de- limiters often have to be segmented for sta- tistical machine translation (SMT). This is commonly performed by automated seg- menters trained on manually annotated corpora. However, the word segmentation (WS) schemes of these annotated corpora are handcrafted for general usage, and may not …...

We examine the embedding approach to reason new relational facts from a large- scale knowledge graph and a text corpus. We propose a novel method of jointly em- bedding entities and words into the same continuous vector space. The embedding process attempts to preserve the relations between entities in the …...

A promising approach to relation extrac- tion, called weak or distant supervision, exploits an existing database of facts as training data, by aligning it to an unla- beled collection of text documents. Using this approach, the task of relation extrac- tion can easily be scaled to hundreds of different relationships. …...

While relation extraction has traditionally been viewed as a task relying solely on textual data, recent work has shown that by taking as input existing facts in the form of entity-relation triples from both knowl- edge bases and textual data, the perfor- mance of relation extraction can be im- proved …...

We introduce a novel compositional lan- guage model that works on Predicate- Argument Structures (PASs). Our model jointly learns word representations and their composition functions using bag- of-words and dependency-based con- texts. Unlike previous word-sequence- based models, our PAS-based model com- poses arguments into predicates by using the category information …...

Broad-coverage relation extraction either requires expensive supervised training data, or suffers from drawbacks inherent to distant supervision. We present an ap- proach for providing partial supervision to a distantly supervised relation extrac- tor using a small number of carefully se- lected examples. We compare against es- tablished active learning criteria …...

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arith- metic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result …...

Many forms of word relatedness have been developed, providing different perspec- tives on word similarity. We introduce a Bayesian probabilistic tensor factoriza- tion model for synthesizing a single word vector representation and per-perspective linear transformations from any number of word similarity matrices. The result- ing word vectors, when combined with …...

Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this pa- per, we focus on a new reading compre- hension task that requires complex reason- ing over a single document. The input is a paragraph …...

We present power low rank ensembles (PLRE), a flexible framework for n-gram language modeling where ensembles of low rank matrices and tensors are used to obtain smoothed probability estimates of words in context. Our method can be understood as a generalization of n- gram modeling to non-integer n, and in- …...

We study the topic dynamics of interac- tions in political debates using the 2012 Republican presidential primary debates as data. We show that the tendency of candidates to shift topics changes over the course of the election campaign, and that it is correlated with their relative power. We also show …...