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        <title>Finding Sub-task Structure with Natural Language Instruction</title>
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            <title>Proceedings of the First Workshop on Learning with Natural Language Supervision</title>
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    <abstract>When mapping a natural language instruction to a sequence of actions, it is often useful toidentify sub-tasks in the instruction. Such sub-task segmentation, however, is not necessarily provided in the training data. We present the A2LCTC (Action-to-Language Connectionist Temporal Classification) algorithm to automatically discover a sub-task segmentation of an action sequence.A2LCTC does not require annotations of correct sub-task segments and learns to find them from pairs of instruction and action sequence in a weakly-supervised manner. We experiment with the ALFRED dataset and show that A2LCTC accurately finds the sub-task structures. With the discovered sub-tasks segments, we also train agents that work on the downstream task and empirically show that our algorithm improves the performance.</abstract>
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%0 Conference Proceedings
%T Finding Sub-task Structure with Natural Language Instruction
%A Ri, Ryokan
%A Hou, Yufang
%A Marinescu, Radu
%A Kishimoto, Akihiro
%Y Andreas, Jacob
%Y Narasimhan, Karthik
%Y Nematzadeh, Aida
%S Proceedings of the First Workshop on Learning with Natural Language Supervision
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ri-etal-2022-finding
%X When mapping a natural language instruction to a sequence of actions, it is often useful toidentify sub-tasks in the instruction. Such sub-task segmentation, however, is not necessarily provided in the training data. We present the A2LCTC (Action-to-Language Connectionist Temporal Classification) algorithm to automatically discover a sub-task segmentation of an action sequence.A2LCTC does not require annotations of correct sub-task segments and learns to find them from pairs of instruction and action sequence in a weakly-supervised manner. We experiment with the ALFRED dataset and show that A2LCTC accurately finds the sub-task structures. With the discovered sub-tasks segments, we also train agents that work on the downstream task and empirically show that our algorithm improves the performance.
%R 10.18653/v1/2022.lnls-1.1
%U https://aclanthology.org/2022.lnls-1.1
%U https://doi.org/10.18653/v1/2022.lnls-1.1
%P 1-9

Markdown (Informal)

[Finding Sub-task Structure with Natural Language Instruction](https://aclanthology.org/2022.lnls-1.1) (Ri et al., LNLS 2022)

• Finding Sub-task Structure with Natural Language Instruction (Ri et al., LNLS 2022)

ACL

• Ryokan Ri, Yufang Hou, Radu Marinescu, and Akihiro Kishimoto. 2022. Finding Sub-task Structure with Natural Language Instruction. In Proceedings of the First Workshop on Learning with Natural Language Supervision, pages 1–9, Dublin, Ireland. Association for Computational Linguistics.