mlsanityData to 14 Oct 2026

Findings of the WMT 2024 Shared Task on Chat Translation

arXiv 2410.11624 ↗First posted 15 Oct 2024cs.CLcited by 2

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Abstract (arXiv)

This paper presents the findings from the third edition of the Chat Translation Shared Task. As with previous editions, the task involved translating bilingual customer support conversations, specifically focusing on the impact of conversation context in translation quality and evaluation. We also include two new language pairs: English-Korean and English-Dutch, in addition to the set of language pairs from previous editions: English-German, English-French, and English-Brazilian Portuguese. We received 22 primary submissions and 32 contrastive submissions from eight teams, with each language pair having participation from at least three teams. We evaluated the systems comprehensively using both automatic metrics and human judgments via a direct assessment framework. The official rankings for each language pair were determined based on human evaluation scores, considering performance in both translation directions--agent and customer. Our analysis shows that while the systems excelled at translating individual turns, there is room for improvement in overall conversation-level translation quality.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

11 of 18 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  3. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  4. 2023GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4cited by 146
  5. 2019Survey on Evaluation Methods for Dialogue Systemscited by 126
  6. 2021A Comprehensive Assessment of Dialog Evaluation Metricscited by 90
  7. 2023Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Modelscited by 87
  8. 2022Quality-Aware Decoding for Neural Machine Translationcited by 49
  9. 2021When Does Translation Require Context? A Data-driven, Multilingual Explorationcited by 36
  10. 2024Is Context Helpful for Chat Translation Evaluation?cited by 5
  11. 2023Dialogue Quality and Emotion Annotations for Customer Support Conversationscited by 4

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.