Exploring the Parameter Space in Statistical Machine Translation via F-structure Transfer

Yvette Graham and Josef van Genabith

Abstract

Machine translation can be carried out via transfer between source and target language deep syntactic structures. In this paper, we examine core parameters of such a system in the context of a statistical approach where the translation model, based on deep syntax, is automatically learned from parsed bilingual corpora. We provide a detailed empirical investigation into the effects of core parameters on translation quality for the German-English translation pair, such as methods of word alignment, limits on the size of transfer rules, transfer decoder beam size, n-best target input representations for generation, as well as deterministic versus non-deterministic generation. Results highlight just how vital employing a suitable method of word alignment is for this approach as well as the significant trade-off between gains in Bleu score and increase in overall translation time that exists when n-best structures are generated.

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