# Context-aware Neural Machine Translation for English-Japanese Business Scene Dialogues

arXiv: https://arxiv.org/abs/2311.11976 · HTML: https://mlsanity.com/paper/2311.11976/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Context-Aware Neural Machine Translation Learns Anaphora Resolution](https://mlsanity.com/paper/1805.10163/) (2018)
- [Neural Machine Translation with Extended Context](https://mlsanity.com/paper/1708.05943/) (2017)
- [Evaluating Discourse Phenomena in Neural Machine Translation](https://mlsanity.com/paper/1711.00513/) (2017)
- [Selective Attention for Context-aware Neural Machine Translation](https://mlsanity.com/paper/1903.08788/) (2019)
- [Measuring and Increasing Context Usage in Context-Aware Machine Translation](https://mlsanity.com/paper/2105.03482/) (2021)
- [Learning to Remember Translation History with a Continuous Cache](https://mlsanity.com/paper/1711.09367/) (2017)
- [When Does Translation Require Context? A Data-driven, Multilingual Exploration](https://mlsanity.com/paper/2109.07446/) (2021)
- [It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information](https://mlsanity.com/paper/2005.02354/) (2020)
- [Modeling Bilingual Conversational Characteristics for Neural Chat Translation](https://mlsanity.com/paper/2107.11164/) (2021)
- [Designing the Business Conversation Corpus](https://mlsanity.com/paper/2008.01940/) (2020)
- [Towards Making the Most of Dialogue Characteristics for Neural Chat Translation](https://mlsanity.com/paper/2109.00668/) (2021)
- [Document-aligned Japanese-English Conversation Parallel Corpus](https://mlsanity.com/paper/2012.06143/) (2020)
- [Scheduled Multi-task Learning for Neural Chat Translation](https://mlsanity.com/paper/2205.03766/) (2022)
- [Zero-pronoun Data Augmentation for Japanese-to-English Translation](https://mlsanity.com/paper/2107.00318/) (2021)

## Built on by (most cited first, 1 of 1)

- [Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding](https://mlsanity.com/paper/2510.06866/) (2025)

## Abstract (arXiv)

Despite the remarkable advancements in machine translation, the current sentence-level paradigm faces challenges when dealing with highly-contextual languages like Japanese. In this paper, we explore how context-awareness can improve the performance of the current Neural Machine Translation (NMT) models for English-Japanese business dialogues translation, and what kind of context provides meaningful information to improve translation. As business dialogue involves complex discourse phenomena but offers scarce training resources, we adapted a pretrained mBART model, finetuning on multi-sentence dialogue data, which allows us to experiment with different contexts. We investigate the impact of larger context sizes and propose novel context tokens encoding extra-sentential information, such as speaker turn and scene type. We make use of Conditional Cross-Mutual Information (CXMI) to explore how much of the context the model uses and generalise CXMI to study the impact of the extra-sentential context. Overall, we find that models leverage both preceding sentences and extra-sentential context (with CXMI increasing with context size) and we provide a more focused analysis on honorifics translation. Regarding translation quality, increased source-side context paired with scene and speaker information improves the model performance compared to previous work and our context-agnostic baselines, measured in BLEU and COMET metrics.