Topic Memory Networks for Short Text Classification

Jichuan Zeng, Jing Li, Yan Song, Cuiyun Gao, Michael R. Lyu, Irwin King


Abstract
Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.
Anthology ID:
D18-1351
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:
3120–3131
Language:
URL:
https://aclanthology.org/D18-1351
DOI:
10.18653/v1/D18-1351
Bibkey:
Cite (ACL):
Jichuan Zeng, Jing Li, Yan Song, Cuiyun Gao, Michael R. Lyu, and Irwin King. 2018. Topic Memory Networks for Short Text Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3120–3131, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Topic Memory Networks for Short Text Classification (Zeng et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1351.pdf
Video:
 https://vimeo.com/305947994