Disentangled representation learning remains challenging as ground truth
factors of variation do not naturally exist. To address this, we present
Vocabulary Disentanglement Retrieval~(VDR), a simple yet effective
retrieval-based disentanglement framework that leverages nature language as
distant supervision. Our approach is built upon the widely-used bi-encoder
architecture with disentanglement heads and is trained on data-text pairs that
are readily available on the web or in existing datasets. This makes our
approach task- and modality-agnostic with potential for a wide range of
downstream applications. We conduct experiments on 16 datasets in both
text-to-text and cross-modal scenarios and evaluate VDR in a zero-shot setting.
With the incorporation of disentanglement heads and a minor increase in
parameters, VDR achieves significant improvements over the base retriever it is
built upon, with a 9% higher on NDCG@10 scores in zero-shot text-to-text
retrieval and an average of 13% higher recall in cross-modal retrieval. In
comparison to other baselines, VDR outperforms them in most tasks, while also
improving explainability and efficiency.

本文提出了一种基于语言远程监督的检索式分离框架，称为 Vocabulary Disentanglement Retrieval~(VDR)，其依赖于生物编码器架构并具有解缠头，经过与其他基线方法的比较，VDR 在大多数任务中都表现优异，并提高了可解释性和效率。