Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding

Samuel Louvan, Bernardo Magnini


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W18-5711.pdf
Data
CoNLL 2003OntoNotes 5.0