mlsanityData to 7 Oct 2026

Learning to Express in Knowledge-Grounded Conversation

arXiv 2204.05805 ↗First posted 12 Apr 2022cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 40 of its 58 references that are in the map. First posted on arXiv 12 Apr 2022.

Builds on

40 of 58 references.

References · 40

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28 more
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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generationcited by 5
  2. 2023ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generationcited by 2