@inproceedings{dash-etal-2020-cs,
title = "{CS}-{NET} at {S}em{E}val-2020 Task 4: {S}iamese {BERT} for {C}om{VE}",
author = "Dash, Soumya Ranjan and
Routray, Sandeep and
Varshney, Prateek and
Modi, Ashutosh",
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-1.61/",
doi = "10.18653/v1/2020.semeval-1.61",
pages = "501--506",
abstract = "In this paper, we describe our system for Task 4 of SemEval 2020, which involves differentiating between natural language statements that conform to common sense and those that do not. The organizers propose three subtasks - first, selecting between two sentences, the one which is against common sense. Second, identifying the most crucial reason why a statement does not make sense. Third, generating novel reasons for explaining the against common sense statement. Out of the three subtasks, this paper reports the system description of subtask A and subtask B. This paper proposes a model based on transformer neural network architecture for addressing the subtasks. The novelty in work lies in the architecture design, which handles the logical implication of contradicting statements and simultaneous information extraction from both sentences. We use a parallel instance of transformers, which is responsible for a boost in the performance. We achieved an accuracy of 94.8{\%} in subtask A and 89{\%} in subtask B on the test set."
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%0 Conference Proceedings
%T CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE
%A Dash, Soumya Ranjan
%A Routray, Sandeep
%A Varshney, Prateek
%A Modi, Ashutosh
%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 dash-etal-2020-cs
%X In this paper, we describe our system for Task 4 of SemEval 2020, which involves differentiating between natural language statements that conform to common sense and those that do not. The organizers propose three subtasks - first, selecting between two sentences, the one which is against common sense. Second, identifying the most crucial reason why a statement does not make sense. Third, generating novel reasons for explaining the against common sense statement. Out of the three subtasks, this paper reports the system description of subtask A and subtask B. This paper proposes a model based on transformer neural network architecture for addressing the subtasks. The novelty in work lies in the architecture design, which handles the logical implication of contradicting statements and simultaneous information extraction from both sentences. We use a parallel instance of transformers, which is responsible for a boost in the performance. We achieved an accuracy of 94.8% in subtask A and 89% in subtask B on the test set.
%R 10.18653/v1/2020.semeval-1.61
%U https://aclanthology.org/2020.semeval-1.61/
%U https://doi.org/10.18653/v1/2020.semeval-1.61
%P 501-506
Markdown (Informal)
[CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE](https://aclanthology.org/2020.semeval-1.61/) (Dash et al., SemEval 2020)
ACL
- Soumya Ranjan Dash, Sandeep Routray, Prateek Varshney, and Ashutosh Modi. 2020. CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 501–506, Barcelona (online). International Committee for Computational Linguistics.