Add `BaryScore`

🚀 Feature

Add BaryScore

Sources:

• Paper: Automatic Text Evaluation through the Lens of Wasserstein Barycenters (EMNLP '21)
• Repo

Motivation

The recent NLG metrics are more often based on BERT (or related) embeddings. As such, I believe, we should also start adding such metrics into TorchMetrics with an extra dependency on transformers if a user wants to use any of these metrics. The BaryScore metric is from a family of untrained metrics (i.e. the model is not fine-tuned on any specific task) so it should be easier for us to begin with it.

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 \texttt{BaryScore} outperforms other BERT based metrics and exhibits more consistent behaviour in particular for text summarization.