Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU

Andrea Vanzo, Emanuele Bastianelli, Oliver Lemon


Abstract
We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures). The architecture is a hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF tagging layers. We describe a variety of experiments, showing that our approach obtains promising results on a dataset annotated with Dialogue Acts and Frame Semantics. Moreover, we demonstrate its applicability to a different, publicly available NLU dataset annotated with domain-specific intents and corresponding semantic roles, providing overall performance higher than state-of-the-art tools such as RASA, Dialogflow, LUIS, and Watson. For example, we show an average 4.45% improvement in entity tagging F-score over Rasa, Dialogflow and LUIS.
Anthology ID:
W19-5931
Volume:
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Month:
September
Year:
2019
Address:
Stockholm, Sweden
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
254–263
Language:
URL:
https://aclanthology.org/W19-5931
DOI:
10.18653/v1/W19-5931
Bibkey:
Cite (ACL):
Andrea Vanzo, Emanuele Bastianelli, and Oliver Lemon. 2019. Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 254–263, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU (Vanzo et al., SIGDIAL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W19-5931.pdf
Data
FrameNet