Xianchao Wu

Semi-Supervised Lexicon Mining from Parenthetical Expressions in Monolingual Web Pages

This paper presents a semi-supervised learning framework for mining Chinese-English lexicons from large amount of Chinese Web pages. The issue is motivated by the observation that many Chinese neologisms are accompanied by their English translations in the form of parenthesis. We classify parenthetical translations into bilingual abbreviations, transliterations, and translations. A frequency-based term recognition approach is applied for extracting bilingual abbreviations. A self-training algorithm is proposed for mining transliteration and translation lexicons. In which, we employ available lexicons in terms of morpheme levels, i.e., phoneme correspondences in transliteration and grapheme (e.g., suffix, stem, and prefix) correspondences in translation. The experimental results verified the effectiveness of our approaches.

Syntax-Based Post-Ordering for Efficient Japanese-to-English Translation

This article proposes a novel reordering method for efficient two-step Japanese-to-English statistical machine translation (SMT) that isolates reordering from SMT and solves it after lexical translation. This reordering problem, called post-ordering, is solved as an SMT problem from Head-Final English (HFE) to English. HFE is syntax-based reordered English that is very successfully used for reordering with English-to-Japanese SMT. The proposed method incorporates its advantage into the reverse direction, Japanese-to-English, and solves the post-ordering problem by accurate syntax-based SMT with target language syntax. Two-step SMT with the proposed post-ordering empirically reduces the decoding time of the accurate but slow syntax-based SMT by its good approximation using intermediate HFE. The proposed method improves the decoding speed of syntax-based SMT decoding by about six times with comparable translation accuracy in Japanese-to-English patent translation experiments.

Improve syntax-based translation using deep syntactic structures

This paper introduces deep syntactic structures to syntax-based Statistical Machine Translation (SMT). We use a Head-driven Phrase Structure Grammar (HPSG) parser to obtain the deep syntactic structures of a sentence, which include not only a fine-grained syntactic property description but also a semantic representation. Considering the abundant information included in the deep syntactic structures, it is interesting to investigate whether or not they improve the traditional syntax-based translation models based on PCFG parsers. In order to use deep syntactic structures for SMT, this paper focuses on extracting tree-to-string translation rules from aligned HPSG tree–string pairs. The major challenge is to properly localize the non-local relations among nodes in an HPSG tree. To localize the semantic dependencies among words and phrases, which can be inherently non-local, a minimum covering tree is defined by taking a predicate word and its lexical/phrasal arguments as the frontier nodes. Starting from this definition, a linear-time algorithm is proposed to extract translation rules through one-time traversal of the leaf nodes in an HPSG tree. Extensive experiments on a tree-to-string translation system testified the effectiveness of our proposal.