Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10475957" target="_blank" >RIV/00216208:11320/23:10475957 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2023.eacl-main.176/" target="_blank" >https://aclanthology.org/2023.eacl-main.176/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2023.eacl-main.176" target="_blank" >10.18653/v1/2023.eacl-main.176</a>

Result language

angličtina

Original language name

Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models

Original language description

Pretrained language models (PLMs) for data-to-text (D2T) generation can use human-readable data labels such as column headings, keys, or relation names to generalize to out-of-domain examples. However, the models are well-known in producing semantically inaccurate outputs if these labels are ambiguous or incomplete, which is often the case in D2T datasets. In this paper, we expose this issue on the task of descibing a relation between two entities. For our experiments, we collect a novel dataset for verbalizing a diverse set of 1,522 unique relations from three large-scale knowledge graphs (Wikidata, DBPedia, YAGO). We find that although PLMs for D2T generation expectedly fail on unclear cases, models trained with a large variety of relation labels are surprisingly robust in verbalizing novel, unseen relations. We argue that using data with a diverse set of clear and meaningful labels is key to training D2T generation systems capable of generalizing to novel domains.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

ISBN

978-1-959429-44-9

ISSN

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e-ISSN

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Number of pages

18

Pages from-to

2398-2415

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg, PA, USA

Event location

Dubrovnik, Croatia

Event date

May 2, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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