mlsanityData to 7 Oct 2026

PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism

arXiv 2212.09086 ↗First posted 18 Dec 2022cs.CLcited by 6

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Abstract (arXiv)

We investigate response generation for multi-turn dialogue in generative-based chatbots. Existing generative models based on RNNs (Recurrent Neural Networks) usually employ the last hidden state to summarize the sequences, which makes models unable to capture the subtle variability observed in different dialogues and cannot distinguish the differences between dialogues that are similar in composition. In this paper, we propose a Pseudo-Variational Gated Recurrent Unit (PVGRU) component without posterior knowledge through introducing a recurrent summarizing variable into the GRU, which can aggregate the accumulated distribution variations of subsequences. PVGRU can perceive the subtle semantic variability through summarizing variables that are optimized by the devised distribution consistency and reconstruction objectives. In addition, we build a Pseudo-Variational Hierarchical Dialogue (PVHD) model based on PVGRU. Experimental results demonstrate that PVGRU can broadly improve the diversity and relevance of responses on two benchmark datasets.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 34 of its 58 references that are in the map. First posted on arXiv 18 Dec 2022.

Builds on

34 of 58 references.

References · 34

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22 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 1.

6 papers.

  1. 2023Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understandingcited by 7
  2. 2025A survey of diversity quantification in natural language processing: The why, what, where and howcited by 4
  3. 2023Evaluate What You Can't Evaluate: Unassessable Quality for Generated Responsecited by 2
  4. 2024It Couldn't Help But Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learningcited by 1
  5. 2024A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generationcited by 1
  6. 2024ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Languagecited by 1