Abstract
Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains. Data-driven dialogue system development has to be adapted to meet this requirement - therefore, reducing the amount of data and annotations necessary for training such systems is a central research problem. In this paper, we present the Dialogue Knowledge Transfer Network (DiKTNet), a state-of-the-art approach to goal-oriented dialogue generation which only uses a few example dialogues (i.e. few-shot learning), none of which has to be annotated. We achieve this by performing a 2-stage training. Firstly, we perform unsupervised dialogue representation pre-training on a large source of goal-oriented dialogues in multiple domains, the MetaLWOz corpus. Secondly, at the transfer stage, we train DiKTNet using this representation together with 2 other textual knowledge sources with different levels of generality: ELMo encoder and the main dataset's source domains. Our main dataset is the Stanford Multi-Domain dialogue corpus. We evaluate our model on it in terms of BLEU and Entity F1 scores, and show that our approach significantly and consistently improves upon a series of baseline models as well as over the previous state-of-the-art dialogue generation model, ZSDG. The improvement upon the latter - up to 10% in Entity F1 and the average of 3% in BLEU score - is achieved using only 10% equivalent of ZSDG's in-domain training data.
Original language | English |
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Title of host publication | Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing |
Publisher | Association for Computational Linguistics |
Pages | 1741-1751 |
Number of pages | 11 |
ISBN (Electronic) | 9781950737901 |
DOIs | |
Publication status | Published - 2019 |
Event | 9th International Joint Conference on Natural Language Processing 2019 - Hong Kong, China Duration: 3 Nov 2019 → 7 Nov 2019 |
Conference
Conference | 9th International Joint Conference on Natural Language Processing 2019 |
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Abbreviated title | EMNLP-IJCNLP 2019 |
Country/Territory | China |
City | Hong Kong |
Period | 3/11/19 → 7/11/19 |
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems