Multi-task Learning by Leveraging the Semantic Information

Abstract

One crucial objective of multi-task learning is to align distributions across tasks so that the information between them can be transferred and shared. However, existing approaches only focused on matching the marginal feature distribution while ignoring the semantic information, which may hinder the learning performance. To address this issue, we propose to leverage the label information in multi-task learning by exploring the semantic conditional relations among tasks. We first theoretically analyze the generalization bound of multi-task learning based on the notion of Jensen-Shannon divergence, which provides new insights into the value of label information in multi-task learning. Our analysis also leads to a concrete algorithm that jointly matches the semantic distribution and controls label distribution divergence. To confirm the effectiveness of the proposed method, we first compare the algorithm with several baselines on some benchmarks and then test the algorithms under label space shift conditions. Empirical results demonstrate that the proposed method could outperform most baselines and achieve state-of-the-art performance, particularly showing the benefits under the label shift conditions.

Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefix

“…Multi-tasking learning (Collobert and Weston, 2008;Zhang and Yang, 2017;Ahmad et al, 2018;Liu et al, 2019a;Zhou et al, 2021) is a common way to incorporate the knowledge of several source tasks into language models. It fine-tunes a language model with multiple objectives for source tasks at the same time.…”

“…We compare our proposed prefix-based training with multi-tasking training (Collobert and Weston, 2008;Zhang and Yang, 2017;Ahmad et al, 2018;Liu et al, 2019a;Zhou et al, 2021). Both approaches are trained on the same source tasks and use pre-trained RoBERTa-large (Liu et al, 2019b).…”