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Detail
Conference Paper
Improving End-to-End Task-Oriented Dialogue System with A Simple Auxiliary Task
- Authors
- Yohan Lee
- Issue Date
- 2021-11
- Citation
- Findings of the Association for Computational Linguistics: EMNLP 2021, pp.1296-1303
- Language
- English
- Type
- Conference Paper
- Abstract
- The paradigm of leveraging large pretrained language models has made significant progress on benchmarks on task-oriented dialogue (TOD) systems. In this paper, we combine this paradigm with multi-task learning framework for end-toend TOD modeling by adopting span prediction as an auxiliary task. In end-toend setting, our model achieves new stateof-the-art results with combined scores of 108.3 and 107.5 on MultiWOZ 2.0 and MultiWOZ 2.1, respectively. Furthermore, we demonstrate that multi-task learning improves not only the performance of model but its generalization capability through domain adaptation experiments in the few-shot setting. The code is available at github.com/bepoetree/MTTOD.
- KSP Keywords
- End to End(E2E), Generalization capability, Language model, Learning framework, Performance of model, Task-oriented, dialogue system, domain adaptation, multi-task learning
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
