Learning multi-lingual sentence embeddings is a fundamental and significant
task in natural language processing. Recent trends of learning both
mono-lingual and multi-lingual sentence embeddings are mainly based on
contrastive learning (CL) with an anchor, one positive, and multiple negative
instances. In this work, we argue that leveraging multiple positives should be
considered for multi-lingual sentence embeddings because (1) positives in a
diverse set of languages can benefit cross-lingual learning, and (2) transitive
similarity across multiple positives can provide reliable structural
information to learn. In order to investigate the impact of CL with multiple
positives, we propose a novel approach MPCL to effectively utilize multiple
positive instances to improve learning multi-lingual sentence embeddings. Our
experimental results on various backbone models and downstream tasks support
that compared with conventional CL, MPCL leads to better retrieval, semantic
similarity, and classification performances. We also observe that on unseen
languages, sentence embedding models trained on multiple positives have better
cross-lingual transferring performance than models trained on a single positive
instance.

本研究旨在通过多个正样本有效利用多语言句子嵌入来改善学习效果，并证实相比传统对比学习，多个正样本可提高句子嵌入在检索、语义相似性和分类任务上的性能，并在未知语言上表现出更好的跨语言迁移性能。