We present MetricBERT, a BERT-based model that learns to embed text under a
well-defined similarity metric while simultaneously adhering to the
``traditional'' masked-language task. We focus on downstream tasks of learning
similarities for recommendations where we show that MetricBERT outperforms
state-of-the-art alternatives, sometimes by a substantial margin. We conduct
extensive evaluations of our method and its different variants, showing that
our training objective is highly beneficial over a traditional contrastive
loss, a standard cosine similarity objective, and six other baselines. As an
additional contribution, we publish a dataset of video games descriptions along
with a test set of similarity annotations crafted by a domain expert.

本文介绍了一种基于 BERT 的模型 MetricBERT，该模型通过学习嵌入文本并同时遵循传统的掩码语言任务，以在定义明确的相似度度量下进行。我们主要关注于推荐中的学习相似性的下游任务，表明 MetricBERT 在超越其他最先进的替代方法并取得显著优势方面出色地表现。此外，我们还发布了一个由领域专家制作的视频游戏描述数据集以及测试集的相似性注释。