# Document-aligned Japanese-English Conversation Parallel Corpus

arXiv: https://arxiv.org/abs/2012.06143 · HTML: https://mlsanity.com/paper/2012.06143/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Modeling Bilingual Conversational Characteristics for Neural Chat Translation](https://mlsanity.com/paper/2107.11164/) (2021)
- [MSCTD: A Multimodal Sentiment Chat Translation Dataset](https://mlsanity.com/paper/2202.13645/) (2022)
- [Towards Making the Most of Dialogue Characteristics for Neural Chat Translation](https://mlsanity.com/paper/2109.00668/) (2021)
- [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)
- [Revisiting Context Choices for Context-aware Machine Translation](https://mlsanity.com/paper/2109.02995/) (2021)
- [Context-aware Neural Machine Translation for English-Japanese Business Scene Dialogues](https://mlsanity.com/paper/2311.11976/) (2023)

## Abstract (arXiv)

Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation. To address the first issue, we present a document-aligned Japanese-English conversation corpus, including balanced, high-quality business conversation data for tuning and testing. As for the second issue, we manually identify the main areas where SL MT fails to produce adequate translations in lack of context. We then create an evaluation set where these phenomena are annotated to alleviate automatic evaluation of DL systems. We train MT models using our corpus to demonstrate how using context leads to improvements.