SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks

Rishivardhan K., Kayalvizhi S, Thenmozhi D., Raghav R., Kshitij Sharma


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.
Anthology ID:
2020.semeval-1.73
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
580–584
Language:
URL:
https://aclanthology.org/2020.semeval-1.73
DOI:
10.18653/v1/2020.semeval-1.73
Bibkey:
Cite (ACL):
Rishivardhan K., Kayalvizhi S, Thenmozhi D., Raghav R., and Kshitij Sharma. 2020. SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 580–584, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks (K. et al., SemEval 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.semeval-1.73.pdf