@inproceedings{srivastava-etal-2020-team,
title = "Team {S}olomon at {S}em{E}val-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning",
author = "Srivastava, Vertika and
Sahoo, Sudeep Kumar and
Kim, Yeon Hyang and
R.r, Rohit and
Raj, Mayank and
Jaiswal, Ajay",
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-1.74",
doi = "10.18653/v1/2020.semeval-1.74",
pages = "585--593",
abstract = "In this paper, we present our submission for SemEval 2020 Task 4 - Commonsense Validation and Explanation (ComVE). The objective of this task was to develop a system that can differentiate statements that make sense from the ones that don{'}t. ComVE comprises of three subtasks to challenge and test a system{'}s capability in understanding commonsense knowledge from various dimensions. Commonsense reasoning is a challenging task in the domain of natural language understanding and systems augmented with it can improve performance in various other tasks such as reading comprehension, and inferencing. We have developed a system that leverages commonsense knowledge from pretrained language models trained on huge corpus such as RoBERTa, GPT2, etc. Our proposed system validates the reasonability of a given statement against the backdrop of commonsense knowledge acquired by these models and generates a logical reason to support its decision. Our system ranked 2nd in subtask C with a BLEU score of 19.3, which by far is the most challenging subtask as it required systems to generate the rationale behind the choice of an unreasonable statement. In subtask A and B, we achieved 96{\%} and 94{\%} accuracy respectively standing at 4th position in both the subtasks.",
}
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<abstract>In this paper, we present our submission for SemEval 2020 Task 4 - Commonsense Validation and Explanation (ComVE). The objective of this task was to develop a system that can differentiate statements that make sense from the ones that don’t. ComVE comprises of three subtasks to challenge and test a system’s capability in understanding commonsense knowledge from various dimensions. Commonsense reasoning is a challenging task in the domain of natural language understanding and systems augmented with it can improve performance in various other tasks such as reading comprehension, and inferencing. We have developed a system that leverages commonsense knowledge from pretrained language models trained on huge corpus such as RoBERTa, GPT2, etc. Our proposed system validates the reasonability of a given statement against the backdrop of commonsense knowledge acquired by these models and generates a logical reason to support its decision. Our system ranked 2nd in subtask C with a BLEU score of 19.3, which by far is the most challenging subtask as it required systems to generate the rationale behind the choice of an unreasonable statement. In subtask A and B, we achieved 96% and 94% accuracy respectively standing at 4th position in both the subtasks.</abstract>
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%0 Conference Proceedings
%T Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning
%A Srivastava, Vertika
%A Sahoo, Sudeep Kumar
%A Kim, Yeon Hyang
%A R.r, Rohit
%A Raj, Mayank
%A Jaiswal, Ajay
%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 srivastava-etal-2020-team
%X In this paper, we present our submission for SemEval 2020 Task 4 - Commonsense Validation and Explanation (ComVE). The objective of this task was to develop a system that can differentiate statements that make sense from the ones that don’t. ComVE comprises of three subtasks to challenge and test a system’s capability in understanding commonsense knowledge from various dimensions. Commonsense reasoning is a challenging task in the domain of natural language understanding and systems augmented with it can improve performance in various other tasks such as reading comprehension, and inferencing. We have developed a system that leverages commonsense knowledge from pretrained language models trained on huge corpus such as RoBERTa, GPT2, etc. Our proposed system validates the reasonability of a given statement against the backdrop of commonsense knowledge acquired by these models and generates a logical reason to support its decision. Our system ranked 2nd in subtask C with a BLEU score of 19.3, which by far is the most challenging subtask as it required systems to generate the rationale behind the choice of an unreasonable statement. In subtask A and B, we achieved 96% and 94% accuracy respectively standing at 4th position in both the subtasks.
%R 10.18653/v1/2020.semeval-1.74
%U https://aclanthology.org/2020.semeval-1.74
%U https://doi.org/10.18653/v1/2020.semeval-1.74
%P 585-593
Markdown (Informal)
[Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning](https://aclanthology.org/2020.semeval-1.74) (Srivastava et al., SemEval 2020)
ACL