MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems

• URL: http://arxiv.org/abs/2009.12005v2
• Date: Mon, 28 Sep 2020 06:43:17 GMT
• Title: MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems
• Authors: Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Pascale Fung
• Abstract summary: We propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems. MinTL is a simple yet effective transfer learning framework, which allows us to plug-and-play pre-trained seq2seq models. We instantiate our learning framework with two pre-trained backbones: T5 and BART, and evaluate them on MultiWOZ.
• Score: 75.43457658815943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data. MinTL is a simple yet effective transfer learning framework, which allows us to plug-and-play pre-trained seq2seq models, and jointly learn dialogue state tracking and dialogue response generation. Unlike previous approaches, which use a copy mechanism to "carryover" the old dialogue states to the new one, we introduce Levenshtein belief spans (Lev), that allows efficient dialogue state tracking with a minimal generation length. We instantiate our learning framework with two pre-trained backbones: T5 and BART, and evaluate them on MultiWOZ. Extensive experiments demonstrate that: 1) our systems establish new state-of-the-art results on end-to-end response generation, 2) MinTL-based systems are more robust than baseline methods in the low resource setting, and they achieve competitive results with only 20\% training data, and 3) Lev greatly improves the inference efficiency.

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