# Designing the Business Conversation Corpus

arXiv: https://arxiv.org/abs/2008.01940 · HTML: https://mlsanity.com/paper/2008.01940/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 14 of 14)

- [JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus](https://mlsanity.com/paper/2202.12607/) (2022)
- [MSCTD: A Multimodal Sentiment Chat Translation Dataset](https://mlsanity.com/paper/2202.13645/) (2022)
- [DP-NMT: Scalable Differentially-Private Machine Translation](https://mlsanity.com/paper/2311.14465/) (2023)
- [Enhancing Translation Accuracy of Large Language Models through Continual Pre-Training on Parallel Data](https://mlsanity.com/paper/2407.03145/) (2024)
- [Towards Speech Dialogue Translation Mediating Speakers of Different Languages](https://mlsanity.com/paper/2305.09210/) (2023)
- [Zero-pronoun Data Augmentation for Japanese-to-English Translation](https://mlsanity.com/paper/2107.00318/) (2021)
- [Chat Translation Error Detection for Assisting Cross-lingual Communications](https://mlsanity.com/paper/2308.01044/) (2023)
- [Generating Diverse Translation with Perturbed kNN-MT](https://mlsanity.com/paper/2402.09344/) (2024)
- [Domain adapted machine translation: What does catastrophic forgetting forget and why?](https://mlsanity.com/paper/2412.17537/) (2024)
- [Context-aware Neural Machine Translation for English-Japanese Business Scene Dialogues](https://mlsanity.com/paper/2311.11976/) (2023)
- [Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation](https://mlsanity.com/paper/2407.18789/) (2024)
- [Zero-shot Cross-lingual Conversational Semantic Role Labeling](https://mlsanity.com/paper/2204.04914/) (2022)
- [Topic-switch adapted Japanese Dialogue System based on PLATO-2](https://mlsanity.com/paper/2302.11280/) (2023)
- [CAT-Translate: Building Compact Open-Source Models for Japanese-English Translation](https://mlsanity.com/paper/2606.21413/) (2026)

## Abstract (arXiv)

While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems. In this paper, we aim to boost the machine translation quality of conversational texts by introducing a newly constructed Japanese-English business conversation parallel corpus. A detailed analysis of the corpus is provided along with challenging examples for automatic translation. We also experiment with adding the corpus in a machine translation training scenario and show how the resulting system benefits from its use.