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.

通过最大化同一文本最小扰动嵌入之间的对齐，以及在更广泛的语料库中鼓励嵌入的均匀分布，基于对比学习的各种方法已被提出来从未标记的数据中学习文本表示。不同的是，我们提出最大化文本和其短语成分组成之间的对齐，考虑了此目标的多种实现方法，并详细阐述了每种情况下对表示的影响。语义文本相似性任务上的实验结果显示，与最先进的方法相当的基线改进。此外，本工作是第一个这样做而不需要额外网络参数或辅助训练目标的工作。