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Span-convert: Few-shot Span Extraction For Dialog With Pretrained Conversational Representations

Sam Coope, Tyler Farghly, Daniela Gerz, Ivan VuliΔ‡, Matthew Henderson . Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 2020 – 43 citations

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ACL Compositional Generalization Few Shot Interdisciplinary Approaches Model Architecture

We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task. This formulation allows for a simple integration of conversational knowledge coded in large pretrained conversational models such as ConveRT (Henderson et al., 2019). We show that leveraging such knowledge in Span-ConveRT is especially useful for few-shot learning scenarios: we report consistent gains over 1) a span extractor that trains representations from scratch in the target domain, and 2) a BERT-based span extractor. In order to inspire more work on span extraction for the slot-filling task, we also release RESTAURANTS-8K, a new challenging data set of 8,198 utterances, compiled from actual conversations in the restaurant booking domain.

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