Chinese-English Translation

SMT systems rely on sufficient amount of parallel corpora to train the trans- lation model. This paper investigates possibilities to use word-to-word and phrase-to-phrase translations extracted not only from clean …...

Word lattice decoding has proven useful in spoken language translation; we argue that it provides a compelling model for translation of text genres, as well. We show that prior work …...

We introduce alignment models for Machine Trans- lation that take into account the context of a source word when determining its translation. Since the use of these contexts alone causes …...

As tokenization is usually ambiguous for many natural languages such as Chinese and Korean, tokenization errors might po- tentially introduce translation mistakes for translation systems that rely on 1-best to- …...

In this paper, we present a method to em- ploy target-side syntactic contextual infor- mation in a Hierarchical Phrase-Based sys- tem. Our method uses Combinatory Cate- gorial Grammar (CCG) to …...

We present a quasi-synchronous dependency grammar (Smith and Eisner, 2006) for ma- chine translation in which the leaves of the tree are phrases rather than words as in pre- vious …...

This paper describes a factored approach to incorporating soft source syntactic constraints into a hierarchical phrase-based translation system. In contrast to traditional approaches that directly introduce syntactic constraints to translation …...

Hiero translation models have two lim- itations compared to phrase-based mod- els: 1) Limited hypothesis space; 2) No lexicalized reordering model. We pro- pose an extension of Hiero called Phrasal- …...

AbstractDependency cohesion refers to the observation that phrases dominated by disjoint dependency subtrees in the source language generally do not overlap in the target language. It has been verified to …...

Recent advances in large-margin learning have shown that better generalization can be achieved by incorporating higher order information into the optimization, such as the spread of the data. However, these …...

We investigate how to improve bilingual embedding which has been successfully used as a feature in phrase-based sta- tistical machine translation (SMT). De- spite bilingual embedding’s success, the contextual information, …...

Data-driven refinement of non-terminal categories has been demonstrated to be a reliable technique for improving mono- lingual parsing with PCFGs. In this pa- per, we extend these techniques to learn …...

Recently, syntactic information has helped significantly to improve statistical ma- chine translation. However, the use of syn- tactic information may have a negative im- pact on the speed of translation …...

While agreement-based joint training has proven to deliver state-of-the-art align- ment accuracy, the produced word align- ments are usually restricted to one-to- one mappings because of the hard cons- traint …...

Lexical selection is of great importance to statistical machine translation. In this paper, we propose a graph-based frame- work for collective lexical selection. The framework is established on a translation …...

Models of neural machine translation are of- ten from a discriminative family of encoder- decoders that learn a conditional distribution of a target sentence given a source sentence. In this …...

AbstractMost neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating …...

AbstractThis paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance. Our proposed models enable attention mechanism …...

AbstractPairwise ranking methods are the most widely used discriminative training approaches for structure prediction problems in natural language processing (NLP). Decomposing the problem of ranking hypotheses into pairwise comparisons enables …...

AbstractNeural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the ground truth …...

AbstractSyntax has been demonstrated highly effective in neural machine translation (NMT). Previous NMT models integrate syntax by representing 1-best tree outputs from a well-trained parsing system, e.g., the representative Tree-RNN …...

AbstractBalancing accuracy and latency is a great challenge for simultaneous translation. To achieve high accuracy, the model usually needs to wait for more streaming text before translation, which results in …...

AbstractText corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English …...

AbstractWe present a multi-task learning framework for cross-lingual abstractive summarization to augment training data. Recent studies constructed pseudo cross-lingual abstractive summarization data to train their neural encoder-decoders. Meanwhile, we introduce …...