mlsanityData to 7 Oct 2026

Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling

arXiv 2204.08152 ↗First posted 18 Apr 2022cs.CLcited by 7

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Abstract (arXiv)

Multi-turn dialogue modeling as a challenging branch of natural language understanding (NLU), aims to build representations for machines to understand human dialogues, which provides a solid foundation for multiple downstream tasks. Recent studies of dialogue modeling commonly employ pre-trained language models (PrLMs) to encode the dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. Therefore, we propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder, which explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 33 of its 44 references that are in the map. First posted on arXiv 18 Apr 2022.

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33 of 44 references.

References · 33

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21 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 5; 2024: 1.

7 papers.

  1. 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 486
  2. 2023GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understandingcited by 10
  3. 2023EM Pre-training for Multi-party Dialogue Response Generationcited by 7
  4. 2023Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contextscited by 6
  5. 2023Pre-training Multi-party Dialogue Models with Latent Discourse Inferencecited by 4
  6. 2024The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claimscited by 1
  7. 2022Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehensioncited by 0