论文标题
上下文神经机器翻译改善了Cataphoric代词的翻译
Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns
论文作者
论文摘要
情境感知的NMT的出现导致了整体翻译质量的有希望的改善，特别是在话语现象（例如代词）的翻译中。以前的作品主要集中于将过去的句子用作上下文的使用，重点是图形翻译。在这项工作中，我们通过将训练有于未来上下文的上下文NMT模型的性能与经过过去的上下文训练的上下文进行了比较，调查了未来句子作为上下文的效果。我们使用以通用和代词为重点的自动指标进行的实验和评估表明，未来上下文的使用不仅可以在上下文 - 不合时宜的变压器上取得重大改进，而且在某些情况下表现出了可比性的，并且在其对应方面对对过去的上下文进行训练的对应方面的性能提高了。我们还对有针对性的cataphora测试套件进行评估，并在BLEU方面报告了对上下文 - 不合时宜的变压器的显着增长。
The advent of context-aware NMT has resulted in promising improvements in the overall translation quality and specifically in the translation of discourse phenomena such as pronouns. Previous works have mainly focused on the use of past sentences as context with a focus on anaphora translation. In this work, we investigate the effect of future sentences as context by comparing the performance of a contextual NMT model trained with the future context to the one trained with the past context. Our experiments and evaluation, using generic and pronoun-focused automatic metrics, show that the use of future context not only achieves significant improvements over the context-agnostic Transformer, but also demonstrates comparable and in some cases improved performance over its counterpart trained on past context. We also perform an evaluation on a targeted cataphora test suite and report significant gains over the context-agnostic Transformer in terms of BLEU.
