论文标题
通过最佳基于运输的对比句子学习，朝着可解释的语义文本相似性
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning
论文作者
论文摘要
最近，对验证句子嵌入之间的相似性的填充语言模型表明，语义文本相似性（STS）任务的最新性能。但是，缺乏句子相似性的解释方法使得很难解释模型输出。在这项工作中，我们明确将句子距离描述为基于运输问题的上下文化令牌距离的加权总和，然后介绍基于最佳的基于运输的距离度量，称为RCMD；它识别和利用语义对齐的令牌对。最后，我们提出了CLRCMD，这是一个优化句子对的对比学习框架，可提高句子相似性及其解释的质量。广泛的实验表明，我们的学习框架在STS和可解释的STS基准上都优于其他基准，这表明它计算有效的句子相似性，还提供了与人类判断一致的解释。代码和检查点可在https://github.com/sh0416/clrcmd上公开获取。
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.
