Automatic Text Evaluation through the Lens of Wasserstein Barycenters
Pierre Colombo, Guillaume Staerman, Chloé Clavel, Pablo Piantanida
Abstract
A new metric BaryScore to evaluate text generation based on deep contextualized embeddings (e.g., BERT, Roberta, ELMo) is introduced. This metric is motivated by a new framework relying on optimal transport tools, i.e., Wasserstein distance and barycenter. By modelling the layer output of deep contextualized embeddings as a probability distribution rather than by a vector embedding; this framework provides a natural way to aggregate the different outputs through the Wasserstein space topology. In addition, it provides theoretical grounds to our metric and offers an alternative to available solutions (e.g., MoverScore and BertScore). Numerical evaluation is performed on four different tasks: machine translation, summarization, data2text generation and image captioning. Our results show that BaryScore outperforms other BERT based metrics and exhibits more consistent behaviour in particular for text summarization.
BibTeX
@inproceedings{colombo-etal-2021-automatic,
title = "Automatic Text Evaluation through the Lens of {W}asserstein Barycenters",
author = "Colombo, Pierre and
Staerman, Guillaume and
Clavel, Chlo{\'e} and
Piantanida, Pablo",
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.817/",
doi = "10.18653/v1/2021.emnlp-main.817",
pages = "10450--10466"
}