Hierarchy-aware Learning of Sequential Tool Usage via Semi-automatically Constructed Taxonomies

Nima Nabizadeh, Martin Heckmann, Dorothea Kolossa


Abstract
When repairing a device, humans employ a series of tools that corresponds to the arrangement of the device components. Such sequences of tool usage can be learned from repair manuals, so that at each step, having observed the previously applied tools, a sequential model can predict the next required tool. In this paper, we improve the tool prediction performance of such methods by additionally taking the hierarchical relationships among the tools into account. To this aim, we build a taxonomy of tools with hyponymy and hypernymy relations from the data by decomposing all multi-word expressions of tool names. We then develop a sequential model that performs a binary prediction for each node in the taxonomy. The evaluation of the method on a dataset of repair manuals shows that encoding the tools with the constructed taxonomy and using a top-down beam search for decoding increases the prediction accuracy and yields an interpretable taxonomy as a potentially valuable byproduct.
Anthology ID:
2020.mwe-1.3
Volume:
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons
Month:
December
Year:
2020
Address:
online
Venues:
COLING | MWE
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
22–26
Language:
URL:
https://www.aclweb.org/anthology/2020.mwe-1.3
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.mwe-1.3.pdf