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---
license: cc-by-sa-4.0
language:
- fr
- en
size_categories:
- n<1K
---
# Dataset Card for DiscEvalMT
## Dataset Details
Contrastive test set for English-to-French MT evaluation covering 2 discourse phenomena: anaphora and lexical choice (coherence/cohesion).
### Dataset Description
For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation, but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. The aim of our article "Evaluating Discourse Phenomena in Neural Machine Translation" was to provide an alternative form of evaluation, specifically targeting discourse phenomena and the need for context beyond the level of the sentence.
- **Curated by:** Rachel Bawden, Rico Sennrich, Alexandra Birch, Alexandra and Barry Haddow
- **Language(s) (NLP):** English-to-French
- **License:** CC-BY-SA-4.0
### Dataset Sources [optional]
- **Repository:** [https://github.com/rbawden/discourse-mt-test-sets/tree/master](https://github.com/rbawden/discourse-mt-test-sets/tree/master)
- **Paper [optional]:** [Bawden et al., 2018. Evaluating Discourse Phenomena in Neural Machine Translation](https://www.aclweb.org/anthology/N18-1118)
## Uses
The dataset is a contrastive evaluation test used to test the use of previous linguistic context (one previous sentence) when translating
from English to French. Each example block is composed of a previous source sentence and two possible translations, accompanied with
accompanying context, which can be used to disambiguate the two possible translations. Each instance in the dataset has a correct translation
and a contrastive one. MT models should be used to rank each translation given the context provided, and the model is evaluated on its
accuracy of ranking the correct translations above the contrastive ones.
### Out-of-Scope Use
The test set is designed for translation from English to French. The test set cannot be used in the opposite direction, as the ambiguity
of the source sentence will not hold. The test set is used to be used as a complement to global metrics of evaluation quality and assumes
that the model has been designed for translation.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
Handcrafted examples (no specific domain) designed to be spontaneous, natural and varied.
#### Data Collection and Processing
Examples were manually created by the first author (native British English speaker, bilingual French) and checked by French native speakers.
Examples were inspired by similar examples found in subtitle data (in terms of syntactic structures, lexical choices, etc.) in order to encourage diversity and naturalness.
#### Personal and Sensitive Information
There is no personal or sensitive information in the dataset.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Citation
**BibTeX:**
```\
@inproceedings{bawden-etal-2018-evaluating,
title = "Evaluating Discourse Phenomena in Neural Machine Translation",
author = "Bawden, Rachel and Sennrich, Rico and Birch, Alexandra and Haddow, Barry",
booktitle = {{Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)}},
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N18-1118",
doi = "10.18653/v1/N18-1118",
pages = "1304--1313"
}
```
**APA:**
Bawden, R., Sennrich, R., Birch, A., & Haddow, B. (2018). Evaluating Discourse Phenomena in Neural Machine Translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 1304–1313). Association for Computational Linguistics.
## Dataset Card Authors
Rachel Bawden: rachel.bawden@inria.fr |