Chenchen Ding

Inter-, Intra-, and Extra-Chunk Pre-Ordering for Statistical Japanese-to-English Machine Translation

A rule-based pre-ordering approach is proposed for statistical Japanese-to-English machine translation using the dependency structure of source-side sentences. A Japanese sentence is pre-ordered to an English-like order at the morpheme level for a statistical machine translation system during the training and decoding phase to resolve the reordering problem. In this article, extra-chunk pre-ordering of morphemes is proposed, which allows Japanese functional morphemes to move across chunk boundaries. This contrasts with the intra-chunk reordering used in previous approaches, which restricts the reordering of morphemes within a chunk. Linguistically oriented discussions show that correct pre-ordering cannot be realized without extra-chunk movement of morphemes. The proposed approach is compared with five rule-based pre-ordering approaches designed for Japanese-to-English translation and with a language independent statistical pre-ordering approach on a standard patent dataset and on a news dataset obtained by crawling Internet news sites. Two state-of-the-art statistical machine translation systems, one phrase-based and the other hierarchical phrase-based, are used in experiments. Experimental results show that the proposed approach outperforms the compared approaches on automatic reordering measures (Kendall’s τ, Spearman’s ρ, fuzzy reordering score, and test set RIBES) and on the automatic translation precision measure of test set BLEU score.

To filter discontinuous word alignment for statistical machine translationaper

We propose a language-independent approach to clean up word alignment errors in an aligned parallel corpus, which are caused by the unsupervised word-align process. In such an aligned corpus, we evaluate the alignment patterns of one-to-many discontinuous words by statistical measures of collocation. The alignment of discontinuous words without strong collocation tendencies will be taken as errors and deleted. We conduct experiments on two-directional Japanese-English and German-English translation tasks. The experiment results show the state-of-the-art word alignment filtered by the proposed approach can lead to a better translation performance.

Dependency Tree Abstraction for Long-Distance Reordering in Statistical Machine Translation

Word reordering is a crucial technique in statistical machine translation in which syntactic information plays an important role. Synchronous context-free grammar has typically been used for this purpose with various modifications for adding flexibilities to its synchronized tree generation. We permit further flexibilities in the synchronous context-free grammar in order to translate between languages with drastically different word order. Our method pre-processes a parallel corpus by