Enhancing End-to-End Conversational Speech Translation Through Target
  Language Context Utilization
Amir Hussein Brian Yan Antonios Anastasopoulos Shinji Watanabe Sanjeev Khudanpur
Incorporating longer context has been shown to benefit machine translation,
but the inclusion of context in end-to-end speech translation (E2E-ST) remains
under-studied. To bridge this gap, we introduce target language context in
E2E-ST, enhancing coherence and overcoming memory constraints of extended audio
segments. Additionally, we propose context dropout to ensure robustness to the
absence of context, and further improve performance by adding speaker
information. Our proposed contextual E2E-ST outperforms the isolated
utterance-based E2E-ST approach. Lastly, we demonstrate that in conversational
speech, contextual information primarily contributes to capturing context
style, as well as resolving anaphora and named entities.