mlsanityData to 7 Oct 2026

It Couldn't Help But Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learning

arXiv 2405.01139 ↗First posted 2 May 2024cs.CLcited by 1

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Abstract (arXiv)

Active participation in a conversation is key to building common ground, since understanding is jointly tailored by producers and recipients. Overhearers are deprived of the privilege of performing grounding acts and can only conjecture about intended meanings. Still, data generation and annotation, modelling, training and evaluation of NLP dialogue models place reliance on the overhearing paradigm. How much of the underlying grounding processes are thereby forfeited? As we show, there is evidence pointing to the impossibility of properly modelling human meta-communicative acts with data-driven learning models. In this paper, we discuss this issue and provide a preliminary analysis on the variability of human decisions for requesting clarification. Most importantly, we wish to bring this topic back to the community's table, encouraging discussion on the consequences of having models designed to only "listen in".

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 90 references that are in the map. First posted on arXiv 2 May 2024.

Builds on

23 of 90 references.

References · 23

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  2. 2017Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoderscited by 222
  3. 2023Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaborationcited by 115
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  5. 2022CLAM: Selective Clarification for Ambiguous Questions with Generative Language Modelscited by 66
  6. 2021Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questionscited by 63
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  9. 2024Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questionscited by 32
  10. 2022Learning to Execute Actions or Ask Clarification Questionscited by 30
  11. 2022Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Qualitycited by 22
  12. 2022Python Code Generation by Asking Clarification Questionscited by 16
11 more
  1. 2021A recipe for annotating grounded clarificationscited by 13
  2. 2023Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructionscited by 13
  3. 2019A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agentscited by 12
  4. 2020Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systemscited by 10
  5. 2022Don't Copy the Teacher: Data and Model Challenges in Embodied Dialoguecited by 10
  6. 2024Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Unitscited by 8
  7. 2022PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanismcited by 6
  8. 2023Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversationscited by 6
  9. 2024Taking Action Towards Graceful Interaction: The Effects of Performing Actions on Modelling Policies for Instruction Clarification Requestscited by 4
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  11. 2023Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Gamescited by 2

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