Transformer Architecture-Based Transfer Learning for Politeness Prediction in Conversation

Shakir Khan (), Mohd Fazil, Agbotiname Lucky Imoize, Bayan Ibrahimm Alabduallah (), Bader M. Albahlal, Saad Abdullah Alajlan, Abrar Almjally and Tamanna Siddiqui
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Shakir Khan: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Mohd Fazil: Center for Transformative Learning, University of Limerick, V94 T9PX Limerick, Ireland
Agbotiname Lucky Imoize: Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
Bayan Ibrahimm Alabduallah: Department of Information System, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia
Bader M. Albahlal: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Saad Abdullah Alajlan: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Abrar Almjally: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Tamanna Siddiqui: Department of Computer Science, Aligarh Muslim University, Aligarh 202002, India

Sustainability, 2023, vol. 15, issue 14, 1-11

Abstract: Politeness is an essential part of a conversation. Like verbal communication, politeness in textual conversation and social media posts is also stimulating. Therefore, the automatic detection of politeness is a significant and relevant problem. The existing literature generally employs classical machine learning-based models like naive Bayes and Support Vector-based trained models for politeness prediction. This paper exploits the state-of-the-art (SOTA) transformer architecture and transfer learning for respectability prediction. The proposed model employs the strengths of context-incorporating large language models, a feed-forward neural network, and an attention mechanism for representation learning of natural language requests. The trained representation is further classified using a softmax function into polite, impolite, and neutral classes. We evaluate the presented model employing two SOTA pre-trained large language models on two benchmark datasets. Our model outperformed the two SOTA and six baseline models, including two domain-specific transformer-based models using both the BERT and RoBERTa language models. The ablation investigation shows that the exclusion of the feed-forward layer displays the highest impact on the presented model. The analysis reveals the batch size and optimization algorithms as effective parameters affecting the model performance.

Keywords: politeness prediction; conversation AI; machine learning; transfer learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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