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Publication Detail
Learning to Execute Actions or Ask Clarification Questions
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Publication Type:Conference
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Authors:Shi Z, Feng Y, Lipani A
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Publication date:10/07/2022
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Name of conference:The 2022 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies
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Conference start date:10/07/2022
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Conference finish date:15/07/2022
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Keywords:cs.CL, cs.CL
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Author URL:
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Notes:Findings of NAACL 2022
Abstract
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.
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