@inproceedings{ri-etal-2022-finding,
title = "Finding Sub-task Structure with Natural Language Instruction",
author = "Ri, Ryokan and
Hou, Yufang and
Marinescu, Radu and
Kishimoto, Akihiro",
editor = "Andreas, Jacob and
Narasimhan, Karthik and
Nematzadeh, Aida",
booktitle = "Proceedings of the First Workshop on Learning with Natural Language Supervision",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.lnls-1.1",
doi = "10.18653/v1/2022.lnls-1.1",
pages = "1--9",
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.",
}
Markdown (Informal)
[Finding Sub-task Structure with Natural Language Instruction](https://aclanthology.org/2022.lnls-1.1) (Ri et al., LNLS 2022)
ACL