Learning to Express in Knowledge-Grounded Conversation
Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses. Existing studies focus on how to synthesize a response with proper knowledge, yet neglect that the same knowledge could be expressed differently by speakers even under the same context. In this work, we mainly consider two aspects of knowledge expression, namely the structure of the response and style of the content in each part. We therefore introduce two sequential latent variables to represent the structure and the content style respectively. We propose a segmentation-based generation model and optimize the model by a variational approach to discover the underlying pattern of knowledge expression in a response. Evaluation results on two benchmarks indicate that our model can learn the structure style defined by a few examples and generate responses in desired content style.
Code (0)
등록된 구현이 없습니다.
Tasks
Dialogue GenerationSimilar Papers 제목 키워드 기반
NewsDialogues: Towards Proactive News Grounded Conversation
Hot news is one of the most popular topics in daily conversations. However, news grounded conversation has long been stymied by the lack of well-designed task definition and scarce data. In this paper, we propose a novel…
Response GenerationBridging Information Gaps in Dialogues With Grounded Exchanges Using Knowledge Graphs
Knowledge models are fundamental to dialogue systems for enabling conversational interactions, which require handling domain-specific knowledge. Ensuring effective communication in information-providing conversations ent…
In-Context LearningKnowledge GraphsZero-Resource Knowledge-Grounded Dialogue Generation
While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often requires knowledge-grounded dialogues tha…
Dialogue GenerationTopical-Chat: Towards Knowledge-Grounded Open-Domain Conversations
Building socialbots that can have deep, engaging open-domain conversations with humans is one of the grand challenges of artificial intelligence (AI). To this end, bots need to be able to leverage world knowledge spannin…
BenchmarkingDecoderWorld KnowledgeKnowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks
While rich, open-domain textual data are generally available and may include interesting phenomena (humor, sarcasm, empathy, etc.) most are designed for language processing tasks, and are usually in a non-conversational …
Data Augmentation