A Dataset for Document Grounded Conversations
This provides a resource for training models to conduct document-grounded conversations, which is incremental as it builds on existing dialogue datasets by adding document context.
The paper tackles the problem of generating conversational responses grounded in document content by introducing a dataset of 4,112 conversations based on Wikipedia movie articles, with an average of 21.43 turns per conversation, and shows that using document information leads to more engaging and fluent responses.
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.