mlsanityData to 7 Oct 2026

A recipe for annotating grounded clarifications

arXiv 2104.08964 ↗First posted 18 Apr 2021cs.CL cs.AIcited by 13

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Abstract (arXiv)

In order to interpret the communicative intents of an utterance, it needs to be grounded in something that is outside of language; that is, grounded in world modalities. In this paper, we argue that dialogue clarification mechanisms make explicit the process of interpreting the communicative intents of the speaker's utterances by grounding them in the various modalities in which the dialogue is situated. This paper frames dialogue clarification mechanisms as an understudied research problem and a key missing piece in the giant jigsaw puzzle of natural language understanding. We discuss both the theoretical background and practical challenges posed by this problem and propose a recipe for obtaining grounding annotations. We conclude by highlighting ethical issues that need to be addressed in future work.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 6 of its 55 references that are in the map. First posted on arXiv 18 Apr 2021.

Builds on

6 of 55 references.

References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
  3. 201912-in-1: Multi-Task Vision and Language Representation Learningcited by 278
  4. 2019Vision-and-Dialog Navigationcited by 246
  5. 2016GuessWhat?! Visual object discovery through multi-modal dialoguecited by 112
  6. 2018Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Informationcited by 110

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 7; 2024: 4; 2025: 1.

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  1. 2023Grounding Gaps in Language Model Generationscited by 45
  2. 2021Grounding 'Grounding' in NLPcited by 44
  3. 2024Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questionscited by 32
  4. 2023Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Datasetcited by 11
  5. 2025AmbiK: Dataset of Ambiguous Tasks in Kitchen Environmentcited by 8
  6. 2024Taking Action Towards Graceful Interaction: The Effects of Performing Actions on Modelling Policies for Instruction Clarification Requestscited by 4
  7. 2023"Are you telling me to put glasses on the dog?'' Content-Grounded Annotation of Instruction Clarification Requests in the CoDraw Datasetcited by 3
  8. 2023'What are you referring to?' Evaluating the Ability of Multi-Modal Dialogue Models to Process Clarificational Exchangescited by 3
  9. 2024Repairs in a Block World: A New Benchmark for Handling User Corrections with Multi-Modal Language Modelscited by 3
  10. 2023Discourse over Discourse: The Need for an Expanded Pragmatic Focus in Conversational AIcited by 1
  11. 2023Pointing out Human Answer Mistakes in a Goal-Oriented Visual Dialoguecited by 1
  12. 2024It Couldn't Help But Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learningcited by 1
1 more
  1. 2023Expanding the Set of Pragmatic Considerations in Conversational AIcited by 0