@inproceedings{chen-etal-2022-mtg,
title = "{MTG}: A Benchmark Suite for Multilingual Text Generation",
author = "Chen, Yiran and
Song, Zhenqiao and
Wu, Xianze and
Wang, Danqing and
Xu, Jingjing and
Chen, Jiaze and
Zhou, Hao and
Li, Lei",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.192",
doi = "10.18653/v1/2022.findings-naacl.192",
pages = "2508--2527",
abstract = "We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first-proposed multilingual multiway text generation dataset with the largest human-annotated data (400k). It includes four generation tasks (story generation, question generation, title generation and text summarization) across five languages (English, German, French, Spanish and Chinese). The multiway setup enables testing knowledge transfer capabilities for a model across languages and tasks. Using MTG, we train and analyze several popular multilingual generation models from different aspects. Our benchmark suite fosters model performance enhancement with more human-annotated parallel data. It provides comprehensive evaluations with diverse generation scenarios. Code and data are available at \url{https://github.com/zide05/MTG}.",
}
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%0 Conference Proceedings
%T MTG: A Benchmark Suite for Multilingual Text Generation
%A Chen, Yiran
%A Song, Zhenqiao
%A Wu, Xianze
%A Wang, Danqing
%A Xu, Jingjing
%A Chen, Jiaze
%A Zhou, Hao
%A Li, Lei
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F chen-etal-2022-mtg
%X We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first-proposed multilingual multiway text generation dataset with the largest human-annotated data (400k). It includes four generation tasks (story generation, question generation, title generation and text summarization) across five languages (English, German, French, Spanish and Chinese). The multiway setup enables testing knowledge transfer capabilities for a model across languages and tasks. Using MTG, we train and analyze several popular multilingual generation models from different aspects. Our benchmark suite fosters model performance enhancement with more human-annotated parallel data. It provides comprehensive evaluations with diverse generation scenarios. Code and data are available at https://github.com/zide05/MTG.
%R 10.18653/v1/2022.findings-naacl.192
%U https://aclanthology.org/2022.findings-naacl.192
%U https://doi.org/10.18653/v1/2022.findings-naacl.192
%P 2508-2527
Markdown (Informal)
[MTG: A Benchmark Suite for Multilingual Text Generation](https://aclanthology.org/2022.findings-naacl.192) (Chen et al., Findings 2022)
ACL
- Yiran Chen, Zhenqiao Song, Xianze Wu, Danqing Wang, Jingjing Xu, Jiaze Chen, Hao Zhou, and Lei Li. 2022. MTG: A Benchmark Suite for Multilingual Text Generation. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2508–2527, Seattle, United States. Association for Computational Linguistics.