Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding

Junyi Li, Wayne Xin Zhao, Ji-Rong Wen, Yang Song


Abstract
Generating long and informative review text is a challenging natural language generation task. Previous work focuses on word-level generation, neglecting the importance of topical and syntactic characteristics from natural languages. In this paper, we propose a novel review generation model by characterizing an elaborately designed aspect-aware coarse-to-fine generation process. First, we model the aspect transitions to capture the overall content flow. Then, to generate a sentence, an aspect-aware sketch will be predicted using an aspect-aware decoder. Finally, another decoder fills in the semantic slots by generating corresponding words. Our approach is able to jointly utilize aspect semantics, syntactic sketch, and context information. Extensive experiments results have demonstrated the effectiveness of the proposed model.
Anthology ID:
P19-1190
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1969–1979
Language:
URL:
https://www.aclweb.org/anthology/P19-1190
DOI:
10.18653/v1/P19-1190
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1190.pdf