@inproceedings{k-etal-2020-ssn,
title = "{SSN}-{NLP} at {S}em{E}val-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks",
author = "K., Rishivardhan and
S, Kayalvizhi and
D., Thenmozhi and
R., Raghav and
Sharma, Kshitij",
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-1.73/",
doi = "10.18653/v1/2020.semeval-1.73",
pages = "580--584",
abstract = "Common sense validation deals with testing whether a system can differentiate natural language statements that make sense from those that do not make sense. This paper describes the our approach to solve this challenge. For common sense validation with multi choice, we propose a stacking based approach to classify sentences that are more favourable in terms of common sense to the particular statement. We have used majority voting classifier methodology amongst three models such as Bidirectional Encoder Representations from Transformers (BERT), Micro Text Classification (Micro TC) and XLNet. For sentence generation, we used Neural Machine Translation (NMT) model to generate explanatory sentences."
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%0 Conference Proceedings
%T SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks
%A K., Rishivardhan
%A S, Kayalvizhi
%A D., Thenmozhi
%A R., Raghav
%A Sharma, Kshitij
%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 k-etal-2020-ssn
%X Common sense validation deals with testing whether a system can differentiate natural language statements that make sense from those that do not make sense. This paper describes the our approach to solve this challenge. For common sense validation with multi choice, we propose a stacking based approach to classify sentences that are more favourable in terms of common sense to the particular statement. We have used majority voting classifier methodology amongst three models such as Bidirectional Encoder Representations from Transformers (BERT), Micro Text Classification (Micro TC) and XLNet. For sentence generation, we used Neural Machine Translation (NMT) model to generate explanatory sentences.
%R 10.18653/v1/2020.semeval-1.73
%U https://aclanthology.org/2020.semeval-1.73/
%U https://doi.org/10.18653/v1/2020.semeval-1.73
%P 580-584
Markdown (Informal)
[SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks](https://aclanthology.org/2020.semeval-1.73/) (K. et al., SemEval 2020)
ACL