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
this https URL.

本文提出一种用于优化句子相似性和解释句子相似性的对比学习框架 CLRCMD，该框架使用 RCMD 方法作为句对的相似度评估方式， RCMD 方法提出了一种通过语境化令牌距离加权和来描述句子距离的方法和解释句子相似性的方法。经过广泛的实验证明，本文所提出的方法在 STS 任务和可解释的 STS 任务上均优于其他基线模型。

基于最优传输对比句子学习的可解释语义文本相似性研究

Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning

It is evident that deep text classification models trained on human data
could be biased. In particular, they produce biased outcomes for texts that
explicitly include identity terms of certain demographic groups. We refer to
this type of bias as explicit bias, which has been extensively studied.
However, deep text classification models can also produce biased outcomes for
texts written by authors of certain demographic groups. We refer to such bias
as implicit bias of which we still have a rather limited understanding. In this
paper, we first demonstrate that implicit bias exists in different text
classification tasks for different demographic groups. Then, we build a
learning-based interpretation method to deepen our knowledge of implicit bias.
Specifically, we verify that classifiers learn to make predictions based on
language features that are related to the demographic attributes of the
authors. Next, we propose a framework Debiased-TC to train deep text
classifiers to make predictions on the right features and consequently mitigate
implicit bias. We conduct extensive experiments on three real-world datasets.
The results show that the text classification models trained under our proposed
framework outperform traditional models significantly in terms of fairness, and
also slightly in terms of classification performance.

本文研究探究深度文本分类模型中的显式和隐式偏差，并提出了一种 Debiased-TC 框架来缓解隐式偏差并提高模型的公平性及分类性能。