Abstract
Automatic Irony Detection refers to making computer understand the real intentions of human behind the ironic language. Much work has been done using classic machine learning techniques applied on various features. In contrast to sophisticated feature engineering, this paper investigates how the deep learning can be applied to the intended task with the help of word embedding. Three different deep learning models, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Attentive RNN, are explored. It shows that the Attentive RNN achieves the state-of-the-art on Twitter datasets. Furthermore, with a closer look at the attention vectors generated by Attentive RNN, an insight into how the attention mechanism helps find out the linguistic clues of ironic utterances is provided.
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Acknowledgements
This research was partially supported by Ministry of Science and Technology, Taiwan, under grants MOST-104-2221-E-002-061-MY3 and MOST-105-2221-E-002-154-MY3.
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Huang, YH., Huang, HH., Chen, HH. (2017). Irony Detection with Attentive Recurrent Neural Networks. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_45
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DOI: https://doi.org/10.1007/978-3-319-56608-5_45
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