通过利用目标语言上下文增强端到端会话语音翻译
Enhancing End-to-End Conversational Speech Translation Through Target Language Context Utilization
AI总结：
针对端到端语音翻译上下文利用不足的问题，本文引入目标语言上下文、上下文丢弃和说话人信息，提升了会话翻译的连贯性与鲁棒性。
摘要：
已有研究表明，纳入更长的上下文有利于机器翻译，但在端到端语音翻译（E2E-ST）中纳入上下文仍未得到充分研究。为弥补这一差距，我们在 E2E-ST 中引入目标语言上下文，以增强连贯性并克服扩展音频片段的内存限制。此外，我们提出上下文丢弃（context dropout），以确保在缺少上下文时的鲁棒性，并通过加入说话人信息进一步提升性能。我们提出的上下文 E2E-ST 优于基于孤立话语的 E2E-ST 方法。最后，我们证明在会话语音中，上下文信息主要有助于捕捉上下文风格，以及消解照应和命名实体。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
