mlsanityData to 14 Oct 2026

Context-Aware Machine Translation with Source Coreference Explanation

arXiv 2404.19505 ↗First posted 30 Apr 2024cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 44 of its 64 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

44 of 64 references.

References · 44

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32 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025The Elephant in the Coreference Room: Resolving Coreference in Full-Length French Fiction Workscited by 4
  2. 2026Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolutioncited by 0