Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations
Samuel Coope, Tyler Farghly, Daniela Gerz, Ivan Vulić, Matthew Henderson
Abstract
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.- Anthology ID:
- 2020.acl-main.11
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 107–121
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.11
- DOI:
- 10.18653/v1/2020.acl-main.11
- Cite (ACL):
- Samuel Coope, Tyler Farghly, Daniela Gerz, Ivan Vulić, and Matthew Henderson. 2020. Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 107–121, Online. Association for Computational Linguistics.
- Cite (Informal):
- Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations (Coope et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.acl-main.11.pdf
- Code
- PolyAI-LDN/task-specific-datasets