2021
EMNLP
EMNLP 2021
Improving Dialogue State Tracking by Joint Slot Modeling
Abstract
AbstractDialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types that share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoz 2.1 from 58.7 to 61.3.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— slot modeling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Natural Language Processing > Generation > Dialogue Systems
Natural Language Processing > Applications > Intent Classification
Artificial Intelligence > Core AI > Reasoning
Natural Language Processing > Applications > Dialogue Systems
Machine Learning > Core Methods > Multi-Task Learning
Artificial Intelligence > Core AI > Dialogue Systems