Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators

Cen Chen, Yinfei Yang, Jun Zhou, Xiaolong Li, Forrest Sheng Bao


Abstract
With the growing amount of reviews in e-commerce websites, it is critical to assess the helpfulness of reviews and recommend them accordingly to consumers. Recent studies on review helpfulness require plenty of labeled samples for each domain/category of interests. However, such an approach based on close-world assumption is not always practical, especially for domains with limited reviews or the “out-of-vocabulary” problem. Therefore, we propose a convolutional neural network (CNN) based model which leverages both word-level and character-based representations. To transfer knowledge between domains, we further extend our model to jointly model different domains with auxiliary domain discriminators. On the Amazon product review dataset, our approach significantly outperforms the state of the art in terms of both accuracy and cross-domain robustness.
Anthology ID:
N18-2095
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
602–607
Language:
URL:
https://aclanthology.org/N18-2095
DOI:
10.18653/v1/N18-2095
Bibkey:
Cite (ACL):
Cen Chen, Yinfei Yang, Jun Zhou, Xiaolong Li, and Forrest Sheng Bao. 2018. Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 602–607, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators (Chen et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-2095.pdf