Automatic Spoken Language Translation Template Acquisition Based on Boosting Structure Extraction and Alignment

In this paper, we propose a new approach for acquiring translation templates automatically from unannotated bilingual spoken language corpora. Two basic algorithms are adopted: a grammar induction algorithm, and an alignment algorithm using Bracketing Transduction Grammar. The approach is unsupervised, statistical, data-driven, and employs no parsing procedure. The acquisition procedure consists of two steps. First, semantic groups and phrase structure groups are extracted from both the source language and the target language through a boosting procedure, in which a synonym dictionary is used to generate the seed groups of the semantic groups. Second, an alignment algorithm based on Bracketing Transduction Grammar aligns the phrase structure groups. The aligned phrase structure groups are post-processed, yielding translation templates. Preliminary experimental results show that the algorithm is effective.