A Dataset for Document Grounded Conversations

Abstract

This paper introduces a document grounded dataset for text conversations. We define "Document Grounded Conversations" as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. This positions this dataset to not only provide a relevant chat history while generating responses but also provide a source of information that the models could use. We describe two neural architectures that provide benchmark performance on the task of generating the next response. We also evaluate our models for engagement and fluency, and find that the information from the document helps in generating more engaging and fluent responses.

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Cited by in corpus (8)

• Zero-Resource Knowledge-Grounded Dialogue Generation
• Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog
• Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?
• RefNet: A Reference-aware Network for Background Based Conversation
• A Large-Scale Chinese Short-Text Conversation Dataset
• Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
• A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots
• A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions for Training Neural Conversation Models