Adding syntactic structure to bilingual terminology for improved domain adaptation

Abstract

Deep-syntax approaches to machine translation have emerged as an alternative to phrase-based statistical systems. TectoMT is an open source framework for transfer-based MT which works at the deep tectogrammatical level and combines linguistic knowledge and statistical techniques. When adapting to a domain, terminological resources improve results with simple techniques, e.g. force-translating domain-specific expressions. In such approaches, multiword entries are translated as if they were a single token-with-spaces, failing to represent the internal structure which makes TectoMT a powerful translation engine. In this work we enrich source and target multiword terms with syntactic structure, and seamlessly integrate them in the tree-based transfer phase of TectoMT. Our experiments on the IT domain using the Microsoft terminological resource show improvement in Spanish, Basque and Portuguese.

Publication
In Proceedings of the 2nd Deep Machine Translation Workshop
Date