@inproceedings{pratapa-etal-2021-evaluating,
title = "Evaluating the Morphosyntactic Well-formedness of Generated Texts",
author = "Pratapa, Adithya and
Anastasopoulos, Antonios and
Rijhwani, Shruti and
Chaudhary, Aditi and
Mortensen, David R. and
Neubig, Graham and
Tsvetkov, Yulia",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.570",
doi = "10.18653/v1/2021.emnlp-main.570",
pages = "7131--7150",
abstract = "Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L{'}AMBRE {--} a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.",
}
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%0 Conference Proceedings
%T Evaluating the Morphosyntactic Well-formedness of Generated Texts
%A Pratapa, Adithya
%A Anastasopoulos, Antonios
%A Rijhwani, Shruti
%A Chaudhary, Aditi
%A Mortensen, David R.
%A Neubig, Graham
%A Tsvetkov, Yulia
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F pratapa-etal-2021-evaluating
%X Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L’AMBRE – a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.
%R 10.18653/v1/2021.emnlp-main.570
%U https://aclanthology.org/2021.emnlp-main.570
%U https://doi.org/10.18653/v1/2021.emnlp-main.570
%P 7131-7150
Markdown (Informal)
[Evaluating the Morphosyntactic Well-formedness of Generated Texts](https://aclanthology.org/2021.emnlp-main.570) (Pratapa et al., EMNLP 2021)
ACL
- Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, and Yulia Tsvetkov. 2021. Evaluating the Morphosyntactic Well-formedness of Generated Texts. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7131–7150, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.