使用个性化语言模型对翻译中语境特异性的无参考分析
Reference-less Analysis of Context Specificity in Translation with Personalised Language Models
AI总结：
本文利用丰富角色与影片元数据构建个性化语言模型，降低困惑度并分析机器翻译的语境特异性，证明语境化模型能更好保留专业翻译的语境特征。
摘要：
使语言模型（LM）对外部语境敏感，有助于它们更有效地捕捉具有特定特征或处于特定环境中的个体的说话模式。本研究考察了在多大程度上可以利用丰富的角色和影片标注，以可扩展的方式实现语言模型的个性化。随后，我们探索了使用此类模型来评估机器翻译中的语境特异性。我们构建了利用丰富语境信息的语言模型；与非语境化模型相比，这些模型最高可将困惑度降低6.5%，并且能够很好地泛化到不存在说话者专属数据的场景，此时模型依赖通过元数据表达的人口统计特征组合。我们的发现在两个语料库上保持一致，其中一个语料库（Cornell-rich）也是本文的贡献之一。随后，我们使用个性化语言模型来衡量机器翻译场景中文本外语境与翻译假设之间的共现情况。结果表明，在我们的领域中，专业翻译具有语境特异性的程度，能够被语境化机器翻译模型比非语境化模型更好地保留；这一点也体现在语境化模型更优的基于参考译文的分数上。
英文摘要：
Sensitising language models (LMs) to external context helps them to more effectively capture the speaking patterns of individuals with specific characteristics or in particular environments. This work investigates to what extent rich character and film annotations can be leveraged to personalise LMs in a scalable manner. We then explore the use of such models in evaluating context specificity in machine translation. We build LMs which leverage rich contextual information to reduce perplexity by up to 6.5% compared to a non-contextual model, and generalise well to a scenario with no speaker-specific data, relying on combinations of demographic characteristics expressed via metadata. Our findings are consistent across two corpora, one of which (Cornell-rich) is also a contribution of this paper. We then use our personalised LMs to measure the co-occurrence of extra-textual context and translation hypotheses in a machine translation setting. Our results suggest that the degree to which professional translations in our domain are context-specific can be preserved to a better extent by a contextual machine translation model than a non-contextual model, which is also reflected in the contextual model's superior reference-based scores.
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。
