A Genre-Aware Attention Model to Improve the Likability Prediction of Books

Suraj Maharjan, Manuel Montes, Fabio A. González, Thamar Solorio


Abstract
Likability prediction of books has many uses. Readers, writers, as well as the publishing industry, can all benefit from automatic book likability prediction systems. In order to make reliable decisions, these systems need to assimilate information from different aspects of a book in a sensible way. We propose a novel multimodal neural architecture that incorporates genre supervision to assign weights to individual feature types. Our proposed method is capable of dynamically tailoring weights given to feature types based on the characteristics of each book. Our architecture achieves competitive results and even outperforms state-of-the-art for this task.
Anthology ID:
D18-1375
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3381–3391
URL:
https://www.aclweb.org/anthology/D18-1375
DOI:
10.18653/v1/D18-1375
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PDF:
https://www.aclweb.org/anthology/D18-1375.pdf