MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems

Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Pascale Fung


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.
Anthology ID:
2020.emnlp-main.273
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3391–3405
Language:
URL:
https://aclanthology.org/2020.emnlp-main.273
DOI:
10.18653/v1/2020.emnlp-main.273
Bibkey:
Cite (ACL):
Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, and Pascale Fung. 2020. MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3391–3405, Online. Association for Computational Linguistics.
Cite (Informal):
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems (Lin et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2020.emnlp-main.273.pdf
Video:
 https://slideslive.com/38938997
Code
 zlinao/MinTL
Data
MultiWOZ