Hyperparameter Optimization for Improving BERT-Based Irony Sentence Recognition

DOI

10.2991/978-94-6463-540-9_64How to use a DOI?

Keywords

Irony Detection; Deep Learning; Neural Network

Abstract

Irony is a figure of speech in which the words are employed with an intended meaning that differs from their literal meaning. The ability to recognize and interpret ironic sentences can prevent misunderstandings in conversations and enhance effective communication. With the continuous improvement of Natural Language Processing (NLP) systems, many issues related to semantic recognition and human-computer dialogue have been largely resolved. However, since ironic sentences have complex semantics and are not easily understood, there are still difficulties in identifying this type of sentences. it can be seen that Irony Detection is of great importance. The experiment of this paper is to establish a recognition model for ironic sentences. Experiments use pytorch library and Bidirectional Encoder Representations from Transformers (BERT) model to train the ironic sentence recognition model. Through neural network and deep learning, the model is successfully trained. Furthermore, the work focuses on examining how various combinations of hyperparameters affect performance. The output model is able to identify sarcastic sentences with success, and it will contribute to the growth of different NLP systems by serving as a useful recognition model.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Renjian Hou
PY  - 2024
DA  - 2024/10/16
TI  - Hyperparameter Optimization for Improving BERT-Based Irony Sentence Recognition
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 634
EP  - 639
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_64
DO  - 10.2991/978-94-6463-540-9_64
ID  - Hou2024
ER  -