Abstract
Slot filling is a crucial task in the Natural Language Understanding (NLU) component of a dialogue system. Most approaches for this task rely solely on the domain-specific datasets for training. We propose a joint model of slot filling and Named Entity Recognition (NER) in a multi-task learning (MTL) setup. Our experiments on three slot filling datasets show that using NER as an auxiliary task improves slot filling performance and achieve competitive performance compared with state-of-the-art. In particular, NER is effective when supervised at the lower layer of the model. For low-resource scenarios, we found that MTL is effective for one dataset.- Anthology ID:
- W18-5711
- Volume:
- Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Aleksandr Chuklin, Jeff Dalton, Julia Kiseleva, Alexey Borisov, Mikhail Burtsev
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 74–80
- Language:
- URL:
- https://aclanthology.org/W18-5711
- DOI:
- 10.18653/v1/W18-5711
- Cite (ACL):
- Samuel Louvan and Bernardo Magnini. 2018. Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding. In Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, pages 74–80, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding (Louvan & Magnini, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/W18-5711.pdf
- Data
- CoNLL 2003, OntoNotes 5.0