何时使用额外上下文：基于证据的术语适应在同步语音翻译中的应用

中文摘要 AI 辅助

额外上下文对技术讲座的同步语音翻译很有价值，但将整个文档上下文注入每个流片段往往过于粗糙。通过诊断实验发现上下文增益主要来自特定论文术语恢复而非统一语义增强。因此提出EGTA框架，它构建文档术语记忆，根据当前流状态选择紧凑候选术语，并仅使用所选术语调整ASR/语音端和解码器端决策空间。EGTA可在多种同步语音翻译设置中实例化，无需全模型微调。在相关评估套件上评估，结果显示其在多个指标上有提升，且改进与特定论文证据对齐相关。

英文摘要

Extra context is valuable for simultaneous speech translation of technical talks, but injecting the entire document context into every streaming segment is often too coarse. Through diagnostic experiments, we find that context gains mainly come from paper-specific terminology recovery rather than uniform semantic enhancement. We therefore propose EGTA, an Evidence-Grounded Terminology Adaptation framework that builds a document terminology memory, selects compact candidate terms conditioned on the current streaming state, and adapts ASR/speech-side and decoder-side decision spaces using only the selected terms. EGTA can be instantiated in cascaded, end-to-end, and generation-only SimulST settings without full-model fine-tuning. We evaluate EGTA on an ACL technical-talk SimulST evaluation suite consisting of MCIF-dev and ACL60/60-dev. On MCIF-dev, EGTA-RG improves BLEU by +1.05/+0.59, XCOMET-XL by +0.019/+0.006, named-entity recall by +79\%/+73\% relative, and acronym recall by +0.099/+0.171 on En$\rightarrow$Zh and En$\rightarrow$De. Across MCIF-dev latency settings, EGTA consistently improves XCOMET-XL, named-entity recall, and acronym recall. External validation on ACL60/60-dev further shows consistent terminology-recall gains without additional fine-tuning. Shuffled-memory controls and activation audits provide evidence that the improvements are tied to paper-specific evidence alignment rather than generic context prompting.

发表机构

• The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））

机构由 AI 辅助整理，请以论文原文为准。