Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
arXiv:1906.02738
Abstract
Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output.
arXiv commentACL 2019 long paper
References in corpus (7)
- Sequence to Sequence Learning with Neural Networks
- Bidirectional Attention Flow for Machine Comprehension
- A Neural Conversational Model
- End-To-End Memory Networks
- Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
- Dialog System Technology Challenge 7
- A Hybrid Retrieval-Generation Neural Conversation Model
Cited by in corpus (6)
- A Survey of Knowledge-Enhanced Text Generation
- Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue
- Challenges in Building Intelligent Open-domain Dialog Systems
- Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
- Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy
- Structuring Latent Spaces for Stylized Response Generation