When Multiwords Go Bad in Machine Translation

This paper addresses the impact of multiword translation errors in machine translation (MT). We have analysed translations of multiwords in the OpenLogos rule-based system (RBMT) and in the Google Translate statistical system (SMT) for the English-French, English-Italian, and English-Portuguese language pairs. Our study shows that, for distinct reasons, multiwords remain a problematic area for MT independently of the approach, and require adequate linguistic quality evaluation metrics founded on a systematic categorization of errors by MT expert linguists. We propose an empirically-driven taxonomy for multiwords, and highlight the need for the development of specific corpora for multiword evaluation. Finally, the paper presents the Logos approach to multiword processing, illustrating how semantico-syntactic rules contribute to multiword translation quality.