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    <abstract>In this article, we try to solve the problem of classification of counterfactual statements and extraction of antecedents/consequences in raw data, by mobilizing on one hand Support vector machine (SVMs) and on the other hand Natural Language Understanding (NLU) infrastructures available on the market for conversational agents. Our experiments allowed us to test different pipelines of two known platforms (Snips NLU and Rasa NLU). The results obtained show that a Rasa NLU pipeline, built with a well-preprocessed dataset and tuned algorithms, allows to model accurately the structure of a counterfactual event, in order to facilitate the identification and the extraction of its components.</abstract>
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%0 Conference Proceedings
%T NLU-Co at SemEval-2020 Task 5: NLU/SVM Based Model Apply Tocharacterise and Extract Counterfactual Items on Raw Data
%A Mboning Tchiaze, Elvis
%A Nouvel, Damien
%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 mboning-tchiaze-nouvel-2020-nlu
%X In this article, we try to solve the problem of classification of counterfactual statements and extraction of antecedents/consequences in raw data, by mobilizing on one hand Support vector machine (SVMs) and on the other hand Natural Language Understanding (NLU) infrastructures available on the market for conversational agents. Our experiments allowed us to test different pipelines of two known platforms (Snips NLU and Rasa NLU). The results obtained show that a Rasa NLU pipeline, built with a well-preprocessed dataset and tuned algorithms, allows to model accurately the structure of a counterfactual event, in order to facilitate the identification and the extraction of its components.
%R 10.18653/v1/2020.semeval-1.87
%U https://aclanthology.org/2020.semeval-1.87/
%U https://doi.org/10.18653/v1/2020.semeval-1.87
%P 670-676

Markdown (Informal)

[NLU-Co at SemEval-2020 Task 5: NLU/SVM Based Model Apply Tocharacterise and Extract Counterfactual Items on Raw Data](https://aclanthology.org/2020.semeval-1.87/) (Mboning Tchiaze & Nouvel, SemEval 2020)

• NLU-Co at SemEval-2020 Task 5: NLU/SVM Based Model Apply Tocharacterise and Extract Counterfactual Items on Raw Data (Mboning Tchiaze & Nouvel, SemEval 2020)

ACL

• Elvis Mboning Tchiaze and Damien Nouvel. 2020. NLU-Co at SemEval-2020 Task 5: NLU/SVM Based Model Apply Tocharacterise and Extract Counterfactual Items on Raw Data. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 670–676, Barcelona (online). International Committee for Computational Linguistics.