# Automatic Construction of Discourse Corpora for Dialogue Translation

arXiv: https://arxiv.org/abs/1605.06770 · HTML: https://mlsanity.com/paper/1605.06770/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2016.

## Built on by (most cited first, 10 of 10)

- [Modeling Bilingual Conversational Characteristics for Neural Chat Translation](https://mlsanity.com/paper/2107.11164/) (2021)
- [TranSmart: A Practical Interactive Machine Translation System](https://mlsanity.com/paper/2105.13072/) (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)
- [A Survey on Zero Pronoun Translation](https://mlsanity.com/paper/2305.10196/) (2023)
- [Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation](https://mlsanity.com/paper/2103.16189/) (2021)
- [Scheduled Multi-task Learning for Neural Chat Translation](https://mlsanity.com/paper/2205.03766/) (2022)
- [A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation](https://mlsanity.com/paper/2301.11749/) (2023)
- [A Context-aware Framework for Translation-mediated Conversations](https://mlsanity.com/paper/2412.04205/) (2024)
- [BaatCheet: A Multilingual Corpus for Dialogue Translation in Indian Languages](https://mlsanity.com/paper/2609.33296/) (2026)

## Abstract (arXiv)

In this paper, a novel approach is proposed to automatically construct parallel discourse corpus for dialogue machine translation. Firstly, the parallel subtitle data and its corresponding monolingual movie script data are crawled and collected from Internet. Then tags such as speaker and discourse boundary from the script data are projected to its subtitle data via an information retrieval approach in order to map monolingual discourse to bilingual texts. We not only evaluate the mapping results, but also integrate speaker information into the translation. Experiments show our proposed method can achieve 81.79% and 98.64% accuracy on speaker and dialogue boundary annotation, and speaker-based language model adaptation can obtain around 0.5 BLEU points improvement in translation qualities. Finally, we publicly release around 100K parallel discourse data with manual speaker and dialogue boundary annotation.