  Recently, finetuning a pretrained language model to capture the similarity
between sentence embeddings has shown the state-of-the-art performance on the
semantic textual similarity (STS) task. However, the absence of an
interpretation method for the sentence similarity makes it difficult to explain
the model output. In this work, we explicitly describe the sentence distance as
the weighted sum of contextualized token distances on the basis of a
transportation problem, and then present the optimal transport-based distance
measure, named RCMD; it identifies and leverages semantically-aligned token
pairs. In the end, we propose CLRCMD, a contrastive learning framework that
optimizes RCMD of sentence pairs, which enhances the quality of sentence
similarity and their interpretation. Extensive experiments demonstrate that our
learning framework outperforms other baselines on both STS and
interpretable-STS benchmarks, indicating that it computes effective sentence
similarity and also provides interpretation consistent with human judgement.
The code and checkpoint are publicly available at
https://github.com/sh0416/clrcmd.
