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About this paper
Han Wu, Kun Xu, Linfeng Song, Lifeng Jin, Haisong Zhang, Linqi Song
Researchers from City University of Hong Kong and Tencent AI Lab systematically explored domain-adaptive pretraining objectives for challenging dialogue understanding tasks, introducing a novel Perturbation Masking Objective (PMO). Their method, which utilizes only the unlabeled portion of the target task's training set for continued pretraining, achieved new state-of-the-art performances on Conversational Semantic Role Labeling and Spoken Language Understanding, improving F1_all by up to 3.2 points and 0.87 points respectively.
Key insights
- Domain-adaptive pretraining, even when restricted to the unlabeled portion of a task's training data, provides substantial benefits for challenging dialogue understanding tasks.
- A combination of general (MLM), span-focused (SBO), and predicate-argument specific (PMO) pretraining objectives synergistically improves model performance on dialogue NLU.
- The novel Perturbation Masking Objective (PMO) is particularly effective at enhancing predicate-argument correlation, which is crucial for resolving complex linguistic phenomena in conversational contexts.
Summary by alphaXiv.
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