@inproceedings{teo-2020-tr,
title = "{TR} at {S}em{E}val-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation",
author = "Teo, Don",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.76",
doi = "10.18653/v1/2020.semeval-1.76",
pages = "601--608",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation
%A Teo, Don
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F teo-2020-tr
%X 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.
%R 10.18653/v1/2020.semeval-1.76
%U https://aclanthology.org/2020.semeval-1.76
%U https://doi.org/10.18653/v1/2020.semeval-1.76
%P 601-608
Markdown (Informal)
[TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation](https://aclanthology.org/2020.semeval-1.76) (Teo, SemEval 2020)
ACL