Content Selection Network for Document-grounded Retrieval-based Chatbots
arXiv:2101.08426
Abstract
Grounding human-machine conversation in a document is an effective way to improve the performance of retrieval-based chatbots. However, only a part of the document content may be relevant to help select the appropriate response at a round. It is thus crucial to select the part of document content relevant to the current conversation context. In this paper, we propose a document content selection network (CSN) to perform explicit selection of relevant document contents, and filter out the irrelevant parts. We show in experiments on two public document-grounded conversation datasets that CSN can effectively help select the relevant document contents to the conversation context, and it produces better results than the state-of-the-art approaches. Our code and datasets are available at https://github.com/DaoD/CSN.
arXiv commentECIR 2021 Camera Ready
References in corpus (9)
- Distributed Representations of Words and Phrases and their Compositionality
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Decoupled Weight Decay Regularization
- Convolutional Neural Network Architectures for Matching Natural Language Sentences
- Topic Aware Neural Response Generation
- Conversational AI: The Science Behind the Alexa Prize
- Personalizing Dialogue Agents: I have a dog, do you have pets too?
- An Information Retrieval Approach to Short Text Conversation
- StarSpace: Embed All The Things!