Unified Multi Intent Order and Slot Prediction using Selective Learning Propagation

Bharatram Natarajan, Priyank Chhipa, Kritika Yadav, Divya Verma Gogoi


Abstract
Natural Language Understanding (NLU) involves two important task namely Intent Determination(ID) and Slot Filling (SF). With recent advancements in Intent Determination and Slot Filling tasks, explorations on handling of multiple intent information in a single utterance is increasing to make the NLU more conversation-based rather than command execution-based. Many have proven this task with huge multi-intent training data. In addition, lots of research have addressed multi intent problem only. The problem of multi intent also poses the challenge of addressing the order of execution of intents found. Hence, we are proposing a unified architecture to address multi-intent detection, associated slotsdetection and order of execution of found intents using low proportion multi-intent corpusin the training data. This architecture consists of Multi Word Importance relation propagator using Multi-Head GRU and Importance learner propagator module using self-attention. This architecture has beaten state-of-the-art by 2.58% on the MultiIntentData dataset.
Anthology ID:
2020.icon-workshop.2
Volume:
Proceedings of the Workshop on Joint NLP Modelling for Conversational AI @ ICON 2020
Month:
December
Year:
2020
Address:
Patna, India
Editors:
Praveen Kumar G S, Siddhartha Mukherjee, Ranjan Samal
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
10–18
Language:
URL:
https://aclanthology.org/2020.icon-workshop.2
DOI:
Bibkey:
Cite (ACL):
Bharatram Natarajan, Priyank Chhipa, Kritika Yadav, and Divya Verma Gogoi. 2020. Unified Multi Intent Order and Slot Prediction using Selective Learning Propagation. In Proceedings of the Workshop on Joint NLP Modelling for Conversational AI @ ICON 2020, pages 10–18, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Unified Multi Intent Order and Slot Prediction using Selective Learning Propagation (Natarajan et al., ICON 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.icon-workshop.2.pdf