Enriching Spoken Language Translation with Dialog Acts

Current statistical speech translation approaches predominantly rely on just text transcripts and do not adequately utilize the rich contextual information such as conveyed through prosody and discourse function. In this paper, we explore the role of context characterized through dialog acts (DAs) in statistical translation. We demonstrate the integration of the dialog acts in a phrase-based statistical translation framework, employing 3 limited domain parallel corpora (Farsi-English, Japanese-English and Chinese-English). For all three language pairs, in addition to producing interpretable DA enriched target language translations, we also obtain improvements in terms of objective evaluation metrics such as lexical selection accuracy and BLEU score.