2018
Maharjan, Suraj; Montes, Manuel; Gonzalez, Fabio A.; Solorio, Thamar
A Genre-Aware Attention Model to Improve the Likability Prediction of Books Proceeding
In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
Abstract | Links | BibTeX | Tags: Genre-Aware Attention Model, Multitask
@proceedings{Maharjan2018b,
title = {A Genre-Aware Attention Model to Improve the Likability Prediction of Books},
author = {Suraj Maharjan and Manuel Montes and Fabio A. Gonzalez and Thamar Solorio},
url = {http://aclweb.org/anthology/D18-1375},
year = {2018},
date = {2018-11-02},
publisher = {In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
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.},
keywords = {Genre-Aware Attention Model, Multitask},
pubstate = {published},
tppubtype = {proceedings}
}
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.