# DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation

arXiv: https://arxiv.org/abs/1905.13354 · HTML: https://mlsanity.com/paper/1905.13354/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 12 of 12)

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- [Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation](https://mlsanity.com/paper/2103.16189/) (2021)
- [MTEB-French: Resources for French Sentence Embedding Evaluation and Analysis](https://mlsanity.com/paper/2405.20468/) (2024)
- [An Interdisciplinary Approach to Human-Centered Machine Translation](https://mlsanity.com/paper/2506.13468/) (2025)
- [DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European Languages](https://mlsanity.com/paper/2310.16749/) (2023)
- [Which Tokens Need Context? A Reference-Based Analysis of Translation Responsibility Using Fertility and Entropy](https://mlsanity.com/paper/2606.29489/) (2026)
- [Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking](https://mlsanity.com/paper/2605.30107/) (2026)
- [BaatCheet: A Multilingual Corpus for Dialogue Translation in Indian Languages](https://mlsanity.com/paper/2609.33296/) (2026)

## Abstract (arXiv)

We present a new English-French test set for the evaluation of Machine Translation (MT) for informal, written bilingual dialogue. The test set contains 144 spontaneous dialogues (5,700+ sentences) between native English and French speakers, mediated by one of two neural MT systems in a range of role-play settings. The dialogues are accompanied by fine-grained sentence-level judgments of MT quality, produced by the dialogue participants themselves, as well as by manually normalised versions and reference translations produced a posteriori. The motivation for the corpus is two-fold: to provide (i) a unique resource for evaluating MT models, and (ii) a corpus for the analysis of MT-mediated communication. We provide a preliminary analysis of the corpus to confirm that the participants' judgments reveal perceptible differences in MT quality between the two MT systems used.