2018
ACL
ACL 2018
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots
Abstract
AbstractWe propose a method that can leverage unlabeled data to learn a matching model for response selection in retrieval-based chatbots. The method employs a sequence-to-sequence architecture (Seq2Seq) model as a weak annotator to judge the matching degree of unlabeled pairs, and then performs learning with both the weak signals and the unlabeled data. Experimental results on two public data sets indicate that matching models get significant improvements when they are learned with the proposed method.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— weak supervision
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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, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Metric Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Natural Language Processing > Applications > Dialogue Systems
Deep Learning > Learning Types > Multi-Modal Learning
Deep Learning > Learning Types > Weakly Supervised Learning
Artificial Intelligence > Core AI > Dialogue Systems