# Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction

arXiv: https://arxiv.org/abs/1911.12091 · HTML: https://mlsanity.com/paper/1911.12091/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation](https://mlsanity.com/paper/2106.00169/) (2021)
- [Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settings](https://mlsanity.com/paper/2103.04225/) (2021)
- [DOLFIN -- Document-Level Financial test set for Machine Translation](https://mlsanity.com/paper/2502.03053/) (2025)
- [Repairing Pronouns in Translation with BERT-Based Post-Editing](https://mlsanity.com/paper/2103.12838/) (2021)
- [Mention Attention for Pronoun Translation](https://mlsanity.com/paper/2412.14829/) (2024)

## Abstract (arXiv)

We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemmatised and PoS-tagged form. We provided four subtasks, for the English-French and English-German language pairs, in both directions. Eleven teams participated in the shared task; nine for the English-French subtask, five for French-English, nine for English-German, and six for German-English. Most of the submissions outperformed two strong language-model based baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs.