用于句子嵌入的成分对比学习
Composition-contrastive Learning for Sentence Embeddings
AI总结：
针对无标注句子表示学习，本文提出文本与其短语成分组合对齐的成分对比学习目标，在语义文本相似度任务上达到可比最先进水平，且无需辅助目标或额外参数。
摘要：
自然语言的向量表示在搜索应用中无处不在。近来，研究者提出了多种基于对比学习的方法，以从无标注数据中学习文本表示：这些方法通过最大化同一文本在最小扰动下所得嵌入之间的对齐度，并促使嵌入在更广泛语料库上呈均匀分布。与之不同，我们提出最大化文本与其短语成分组合之间的对齐度。我们考察了该目标的若干实现形式，并详细说明了每种情形对表示的影响。在语义文本相似度任务上的实验结果表明，相较基线方法取得了提升，且性能可与最先进方法相媲美。此外，本工作首次在不承担辅助训练目标或额外网络参数成本的情况下实现这一点。
英文摘要：
Vector representations of natural language are ubiquitous in search applications. Recently, various methods based on contrastive learning have been proposed to learn textual representations from unlabelled data; by maximizing alignment between minimally-perturbed embeddings of the same text, and encouraging a uniform distribution of embeddings across a broader corpus. Differently, we propose maximizing alignment between texts and a composition of their phrasal constituents. We consider several realizations of this objective and elaborate the impact on representations in each case. Experimental results on semantic textual similarity tasks show improvements over baselines that are comparable with state-of-the-art approaches. Moreover, this work is the first to do so without incurring costs in auxiliary training objectives or additional network parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
