TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation

Don Teo


Abstract
In this paper, we present our submission for subtask A of the Common Sense Validation and Explanation (ComVE) shared task. We examine the ability of large-scale pre-trained language models to distinguish commonsense from non-commonsense statements. We also explore the utility of external resources that aim to supplement the world knowledge inherent in such language models, including commonsense knowledge graph embedding models, word concreteness ratings, and text-to-image generation models. We find that such resources provide insignificant gains to the performance of fine-tuned language models. We also provide a qualitative analysis of the limitations of the language model fine-tuned to this task.
Anthology ID:
2020.semeval-1.76
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
601–608
Language:
URL:
https://aclanthology.org/2020.semeval-1.76
DOI:
10.18653/v1/2020.semeval-1.76
Bibkey:
Cite (ACL):
Don Teo. 2020. TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 601–608, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation (Teo, SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.76.pdf
Data
ConceptNetMS COCO