In this paper we propose four deep learning models for the task of detecting and classifying Patronizing and Condescending Language (PCL) using a corpus of over 13,000 annotated paragraphs in English. The task, hosted at SemEval-2022, consists of two different subtasks. The Subtask 1 is a binary classification problem. Namely, given a paragraph, a system must predict whether or not it contains any form of PCL. The Subtask 2 is a multi-label classification task. Given a paragraph, a system must identify which PCL categories express the condescension. A paragraph might contain one or more categories of PCL. To face with the first subtask we propose a multi-channel Convolutional Neural Network (CNN) and an Hybrid LSTM. Using the multi-channel CNN we explore the impact of parallel word emebeddings and convolutional layers involving different kernel sizes. With Hybrid LSTM we focus on extracting features in advance, thanks to a convolutional layer followed by two bidirectional LSTM layers. For the second subtask a Transformer BERT-based model (i.e. DistilBERT) and an XLNet-based model are proposed. The multi-channel CNN model is able to reach an F1 score of 0.2928, the Hybrid LSTM modelis able to reach an F1 score of 0.2815, the DistilBERT-based one an average F1 of 0.2165 and the XLNet an average F1 of 0.2296. In this paper, in addition to system descriptions, we also provide further analysis of the results, highlighting strengths and limitations.We make all the code publicly available and reusable on GitHub.

Siino, M., La Cascia, M., Tinnirello, I. (2022). McRock at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNet. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 409-417) [10.18653/v1/2022.semeval-1.55].

McRock at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNet

Siino, Marco
;
La Cascia, Marco;Tinnirello, Ilenia
2022

Abstract

In this paper we propose four deep learning models for the task of detecting and classifying Patronizing and Condescending Language (PCL) using a corpus of over 13,000 annotated paragraphs in English. The task, hosted at SemEval-2022, consists of two different subtasks. The Subtask 1 is a binary classification problem. Namely, given a paragraph, a system must predict whether or not it contains any form of PCL. The Subtask 2 is a multi-label classification task. Given a paragraph, a system must identify which PCL categories express the condescension. A paragraph might contain one or more categories of PCL. To face with the first subtask we propose a multi-channel Convolutional Neural Network (CNN) and an Hybrid LSTM. Using the multi-channel CNN we explore the impact of parallel word emebeddings and convolutional layers involving different kernel sizes. With Hybrid LSTM we focus on extracting features in advance, thanks to a convolutional layer followed by two bidirectional LSTM layers. For the second subtask a Transformer BERT-based model (i.e. DistilBERT) and an XLNet-based model are proposed. The multi-channel CNN model is able to reach an F1 score of 0.2928, the Hybrid LSTM modelis able to reach an F1 score of 0.2815, the DistilBERT-based one an average F1 of 0.2165 and the XLNet an average F1 of 0.2296. In this paper, in addition to system descriptions, we also provide further analysis of the results, highlighting strengths and limitations.We make all the code publicly available and reusable on GitHub.
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
https://aclanthology.org/2022.semeval-1.55/
Siino, M., La Cascia, M., Tinnirello, I. (2022). McRock at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNet. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 409-417) [10.18653/v1/2022.semeval-1.55].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/566502
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