mlsanityData to 7 Oct 2026

Learning to Execute Actions or Ask Clarification Questions

arXiv 2204.08373 ↗First posted 18 Apr 2022cs.CLcited by 30

Not on the map. Why?

Abstract (arXiv)

Collaborative tasks are ubiquitous activities where a form of communication is required in order to reach a joint goal. Collaborative building is one of such tasks. We wish to develop an intelligent builder agent in a simulated building environment (Minecraft) that can build whatever users wish to build by just talking to the agent. In order to achieve this goal, such agents need to be able to take the initiative by asking clarification questions when further information is needed. Existing works on Minecraft Corpus Dataset only learn to execute instructions neglecting the importance of asking for clarifications. In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions. Experimental results show that our model achieves state-of-the-art performance on the collaborative building task with a substantial improvement. We also define two new tasks, the learning to ask task and the joint learning task. The latter consists of solving both collaborating building and learning to ask tasks jointly.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 18 Apr 2022.

Builds on

27 of 45 references.

References · 27

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  4. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  5. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
  6. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
  7. 2019Vision-and-Dialog Navigationcited by 246
  8. 2018Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environmentscited by 238
  9. 2019Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systemscited by 215
  10. 2019MineRL: A Large-Scale Dataset of Minecraft Demonstrationscited by 210
  11. 2016GuessWhat?! Visual object discovery through multi-modal dialoguecited by 112
  12. 2019Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learningcited by 100
15 more
  1. 2017Mapping Instructions and Visual Observations to Actions with Reinforcement Learningcited by 80
  2. 2017Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learningcited by 77
  3. 2019Executing Instructions in Situated Collaborative Interactionscited by 68
  4. 2019Just Ask:An Interactive Learning Framework for Vision and Language Navigationcited by 53
  5. 2020A Sequence-to-Sequence Approach to Dialogue State Trackingcited by 43
  6. 2020Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representationscited by 42
  7. 2020RMM: A Recursive Mental Model for Dialog Navigationcited by 32
  8. 2017Learning by Asking Questionscited by 25
  9. 2017Representation Learning for Grounded Spatial Reasoningcited by 23
  10. 2022Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Trackingcited by 18
  11. 2019Why Build an Assistant in Minecraft?cited by 14
  12. 2018Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observationcited by 12
  13. 2020Robust and Interpretable Grounding of Spatial References with Relation Networkscited by 8
  14. 2021Leveraging Review Properties for Effective Recommendationcited by 7
  15. 2020Negative Confidence-Aware Weakly Supervised Binary Classification for Effective Review Helpfulness Classificationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 14; 2024: 13; 2025: 1; 2026: 1.

30 papers.

Sort:

By area:

  1. 2024Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agentscited by 71
  2. 2024Instruction Tuning With Loss Over Instructionscited by 40
  3. 2023DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningcited by 34
  4. 2024Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questionscited by 32
  5. 2023Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedbackcited by 31
  6. 2023Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learnercited by 23
  7. 2024A Survey of Conversational Searchcited by 22
  8. 2023Rethinking Semi-supervised Learning with Language Modelscited by 17
  9. 2023Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Datasetcited by 11
  10. 2024ArchCode: Incorporating Software Requirements in Code Generation with Large Language Modelscited by 8
  11. 2024RAcQUEt: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMscited by 8
  12. 2024Large Language Models as Minecraft Agentscited by 7
18 more
  1. 2023When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solutioncited by 5
  2. 2023R2H: Building Multimodal Navigation Helpers that Respond to Help Requestscited by 5
  3. 2024Taking Action Towards Graceful Interaction: The Effects of Performing Actions on Modelling Policies for Instruction Clarification Requestscited by 4
  4. 2024Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraftcited by 4
  5. 2023Self Contrastive Learning for Session-based Recommendationcited by 3
  6. 2023"Are you telling me to put glasses on the dog?'' Content-Grounded Annotation of Instruction Clarification Requests in the CoDraw Datasetcited by 3
  7. 2023Towards a Neural Era in Dialogue Management for Collaboration: A Literature Surveycited by 3
  8. 2023'What are you referring to?' Evaluating the Ability of Multi-Modal Dialogue Models to Process Clarificational Exchangescited by 3
  9. 2024Nebula: A discourse aware Minecraft Buildercited by 3
  10. 2024Repairs in a Block World: A New Benchmark for Handling User Corrections with Multi-Modal Language Modelscited by 3
  11. 2025It Depends: Resolving Referential Ambiguity in Minimal Contexts with Commonsense Knowledgecited by 3
  12. 2023Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysiscited by 2
  13. 2024It Couldn't Help But Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learningcited by 1
  14. 2022Attention-based Ingredient Phrase Parsercited by 0
  15. 2023Solving Dialogue Grounding Embodied Task in a Simulated Environment using Further Masked Language Modelingcited by 0
  16. 2023Lexical Entrainment for Conversational Systemscited by 0
  17. 2024Improving Agent Interactions in Virtual Environments with Language Modelscited by 0
  18. 2026Beyond Local Surprise: Grounded Dialogue as Selective Belief Revision under Referential Uncertaintycited by 0