Context-Aware Machine Translation with Source Coreference Explanation
This addresses a specific bottleneck in document-level machine translation for NLP researchers, though it is incremental as it builds on existing MT models.
The paper tackles the problem of context-aware machine translation models underperforming due to the explain-away effect by proposing a model that predicts coreference features to explain translation decisions, resulting in an improvement of over 1.0 BLEU score compared to other context-aware models.
Despite significant improvements in enhancing the quality of translation, context-aware machine translation (MT) models underperform in many cases. One of the main reasons is that they fail to utilize the correct features from context when the context is too long or their models are overly complex. This can lead to the explain-away effect, wherein the models only consider features easier to explain predictions, resulting in inaccurate translations. To address this issue, we propose a model that explains the decisions made for translation by predicting coreference features in the input. We construct a model for input coreference by exploiting contextual features from both the input and translation output representations on top of an existing MT model. We evaluate and analyze our method in the WMT document-level translation task of English-German dataset, the English-Russian dataset, and the multilingual TED talk dataset, demonstrating an improvement of over 1.0 BLEU score when compared with other context-aware models.