Automatic Text Evaluation through the Lens of Wasserstein Barycenters

• URL: http://arxiv.org/abs/2108.12463v1
• Date: Fri, 27 Aug 2021 19:08:52 GMT
• Title: Automatic Text Evaluation through the Lens of Wasserstein Barycenters
• Authors: Pierre Colombo, Guillaume Staerman, Chloe Clavel, Pablo Piantanida
• Abstract summary: A new metric textttBaryScore is introduced to evaluate text generation based on deep contextualized embeddings. Our results show that textttBaryScore outperforms other BERT based metrics and exhibits more consistent behaviour in particular for text summarization.
• Score: 24.71226781348407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A new metric \texttt{BaryScore} to evaluate text generation based on deep contextualized embeddings (\textit{e.g.}, BERT, Roberta, ELMo) is introduced. This metric is motivated by a new framework relying on optimal transport tools, \textit{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 (\textit{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 \texttt{BaryScore} outperforms other BERT based metrics and exhibits more consistent behaviour in particular for text summarization.

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