Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
arXiv:2103.09534
Abstract
Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context utterances and response. However, in real-place conversation scenarios, whether a response candidate is suitable not only counts on the given dialogue context but also other backgrounds, e.g., wording habits, user-specific dialogue history content. To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN). Our contributions are two-fold: 1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information; 2) we perform hybrid representation learning on context-response utterances and explicitly incorporate a customized attention mechanism to extract vital information from context-response interactions so as to improve the accuracy of matching. We evaluate our model on two large datasets with user identification, i.e., personalized Ubuntu dialogue Corpus (P-Ubuntu) and personalized Weibo dataset (P-Weibo). Experimental results confirm that our method significantly outperforms several strong models by combining personalized attention, wording behaviors, and hybrid representation learning.
arXiv commentAccepted by ACM Transactions on Information Systems, 25 pages, 2 figures, 9 tables
References in corpus (12)
- Recurrent Neural Network for Text Classification with Multi-Task Learning
- Convolutional Neural Network Architectures for Matching Natural Language Sentences
- A Deep Relevance Matching Model for Ad-hoc Retrieval
- End-to-End Neural Ad-hoc Ranking with Kernel Pooling
- An Information Retrieval Approach to Short Text Conversation
- Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
- When Are Tree Structures Necessary for Deep Learning of Representations?
- Hierarchical Recurrent Attention Network for Response Generation
- Modeling Multi-turn Conversation with Deep Utterance Aggregation
- Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
- Deep Retrieval-Based Dialogue Systems: A Short Review
- Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots