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)
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.semeval-1.76.pdf
- Data
- COCO, ConceptNet