Dialogue State Tracking with a Language Model using Schema-Driven Prompting
Published in EMNLP, 2021
Chia-Hsuan Lee, Hao Cheng, Mari Ostendorf [PDF]
@inproceedings{lee-etal-2021-dialogue, title = "Dialogue State Tracking with a Language Model using Schema-Driven Prompting", author = "Lee, Chia-Hsuan and Cheng, Hao and Ostendorf, Mari", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.404", pages = "4937--4949", abstract = "Task-oriented conversational systems often use dialogue state tracking to represent the user{'}s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available at https://github.com/chiahsuan156/DST-as-Prompting.", }