@inproceedings{eichel-etal-2023-made,
title = "Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain",
author = "Eichel, Annerose and
Schlipf, Helena and
Schulte im Walde, Sabine",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.104",
doi = "10.18653/v1/2023.eacl-main.104",
pages = "1420--1435",
abstract = "We propose a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain by probing Pretrained Language Models (PLMs) in a cloze task style setting to overcome the lack of annotated datasets. We devise a new method to aggregate salient predictions from a set of cloze query templates and show that domain-adaptation using either a small, high-quality or a customized Wikipedia corpus boosts performance. When exploring resource-lean alternatives, we find a distilled PLM clearly outperforming a classic pattern-based algorithm. Further, given that 98{\%} of our domain-specific components are multiword expressions, we successfully exploit the compositionality assumption as a way to address data sparsity.",
}
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%0 Conference Proceedings
%T Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain
%A Eichel, Annerose
%A Schlipf, Helena
%A Schulte im Walde, Sabine
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F eichel-etal-2023-made
%X We propose a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain by probing Pretrained Language Models (PLMs) in a cloze task style setting to overcome the lack of annotated datasets. We devise a new method to aggregate salient predictions from a set of cloze query templates and show that domain-adaptation using either a small, high-quality or a customized Wikipedia corpus boosts performance. When exploring resource-lean alternatives, we find a distilled PLM clearly outperforming a classic pattern-based algorithm. Further, given that 98% of our domain-specific components are multiword expressions, we successfully exploit the compositionality assumption as a way to address data sparsity.
%R 10.18653/v1/2023.eacl-main.104
%U https://aclanthology.org/2023.eacl-main.104
%U https://doi.org/10.18653/v1/2023.eacl-main.104
%P 1420-1435
Markdown (Informal)
[Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain](https://aclanthology.org/2023.eacl-main.104) (Eichel et al., EACL 2023)
ACL