Learning List-Level Domain-Invariant Representations for Ranking

TL;DR

This paper introduces list-level domain-invariant representation learning for ranking, providing theoretical generalization bounds and improved empirical transfer performance in unsupervised domain adaptation tasks.

Contribution

It proposes a novel list-level alignment method that leverages list structure, offering the first theoretical generalization bound for ranking domain adaptation.

Findings

Achieves better transfer performance on ranking tasks.

Provides the first theoretical generalization bound for ranking domain adaptation.

Outperforms previous item-level alignment methods.

Abstract

Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning, which matches and aligns the data distributions on the feature space. Although this method is studied extensively and applied on classification and regression problems, its adoption on ranking problems is sporadic, and the few existing implementations lack theoretical justifications. This paper revisits invariant representation learning for ranking. Upon reviewing prior work, we found that they implement what we call item-level alignment, which aligns the distributions of the items being ranked from all lists in aggregate but ignores their list structure. However, the list structure should be leveraged, because it is intrinsic to ranking problems where the data and the metrics are defined and computed on lists, not the items by themselves. To close this discrepancy, we propose list-level alignment -- learning domain-invariant representations at the higher level of lists. The benefits are twofold: it leads to the first domain adaptation generalization bound for ranking, in turn providing theoretical support for the proposed method, and it achieves better empirical transfer performance for unsupervised domain adaptation on ranking tasks, including passage reranking.