Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

by   Liu Yang, et al.

Intelligent personal assistant systems with either text-based or voice-based conversational interfaces are becoming increasingly popular around the world. Retrieval-based conversation models have the advantages of returning fluent and informative responses. Most existing studies in this area are on open domain "chit-chat" conversations or task / transaction oriented conversations. More research is needed for information-seeking conversations. There is also a lack of modeling external knowledge beyond the dialog utterances among current conversational models. In this paper, we propose a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems. We incorporate external knowledge into deep neural models with pseudo-relevance feedback and QA correspondence knowledge distillation. Extensive experiments with three information-seeking conversation data sets including both open benchmarks and commercial data show that, our methods outperform various baseline methods including several deep text matching models and the state-of-the-art method on response selection in multi-turn conversations. We also perform analysis over different response types, model variations and ranking examples. Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.


page 1

page 2

page 3

page 4


IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon...

A Hybrid Retrieval-Generation Neural Conversation Model

Intelligent personal assistant systems, with either text-based or voice-...

Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations

Intelligent personal assistant systems for information-seeking conversat...

Towards More Realistic Generation of Information-Seeking Conversations

In this paper, we introduce a novel framework SimSeek (simulating inform...

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce

Building multi-turn information-seeking conversation systems is an impor...

INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions

In an information-seeking conversation, a user converses with an agent t...

Multimodal Dialogs (MMD): A large-scale dataset for studying multimodal domain-aware conversations

While multimodal conversation agents are gaining importance in several d...

Please sign up or login with your details

Forgot password? Click here to reset