Compositional Zero-Shot Domain Transfer with Text-to-Text Models
Abstract
Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero-shot T5) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from MLM of unlabelled in-domain free text) and task knowledge (from task training on more readily available general-domain data) in a multi-task manner. To improve the transferability of task training, we design a strategy named NLGU: we simultaneously train NLG for in-domain label-to-data generation which enables data augmentation for self-finetuning and NLU for label prediction. We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning. DoT5 demonstrates the effectiveness of compositional transfer learning through multi-task learning. In particular, DoT5 outperforms the current SOTA in zero-shot transfer by over 7 absolute points in accuracy on RadNLI. We validate DoT5 with ablations and a case study demonstrating its ability to solve challenging NLI examples requiring in-domain expertise.
Cite
@article{arxiv.2303.13386,
title = {Compositional Zero-Shot Domain Transfer with Text-to-Text Models},
author = {Fangyu Liu and Qianchu Liu and Shruthi Bannur and Fernando Pérez-García and Naoto Usuyama and Sheng Zhang and Tristan Naumann and Aditya Nori and Hoifung Poon and Javier Alvarez-Valle and Ozan Oktay and Stephanie L. Hyland},
journal= {arXiv preprint arXiv:2303.13386},
year = {2023}
}
Comments
Accepted at TACL, pre-MIT Press publication version. 16 pages, 4 figures