Conditional-Balanced Adversarial Delta Tuning for Cross-Domain Implicit Discourse Relation Recognition

Abstract

Implicit discourse relation recognition (IDRR) is faced with a domain dilemma. Recent studies have achieved breakthroughs in standard datasets, while they are not appropriate in domains with insufficient data, such as bio-medicine. In this paper, we treat this problem as a cross-domain IDRR task, which transfers knowledge from the source domain to improve the understanding of the target domain. However, cross-domain IDRR is supervised and suffers from striking domain gaps, and general domain adaptation methods are not applicable. Therefore, we propose a Conditional-Balanced Adversarial Delta Tuning (CBADT) framework, which 1) leverages delta tuning to mine the dense domain-specific knowledge in low-resource scenarios; 2) builds a conditional adversarial verbalizer to inject domain-invariant knowledge into soft prompts; 3) proposes a domain label fusion method to balance domain-specific label words and fit the hybrid features from two domains. Experiments on both Chinese and English corpora demonstrate the transferability of our model.