Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach

By Igor Shalyminov, Sungjin Lee, Arash Eshghi, Oliver Lemon. 2019

25 citations recorded by the source corpus.

Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems. In a real-world enterprise setting where dialogue systems are developed rapidly and are expected to work robustly for an ever-growing variety of domains, products, and scenarios, efficient learning from a limited number of examples becomes indispensable. In this paper, we introduce a technique to achieve state-of-the-art dialogue generation performance in a few-shot setup, without using any annotated data. We do this by leveraging background knowledge from a larger, more highly represented dialogue source — namely, the MetaLWOz dataset. We evaluate our model on the Stanford Multi-Domain Dialogue Dataset, consisting of human-human goal-oriented dialogues in in-car navigation, appointment scheduling, and weather information domains. We show that our few-shot approach achieves state-of-the art results on that dataset by consistently outperforming the previous best model in terms of BLEU and Entity F1 scores, while being more data-efficient than it by not requiring any data annotation.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations (2021, background): “In the more general area of natural language generation, few-shot learning is a growing topic (Chen et al., 2020), e.g., in dialog generation (Peng et al., 2020; Shalyminov et al., 2019).”
Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer (2020, background): “Later, Shalyminov et al. [6, 7] proposed Dialogue Knowledge Transfer Networks which approached the problem in a few-shot setup with a separate out-of-domain pre-training stage on a large goal-oriented corpus (MetaLWOz, [8]).”