Learning with Noisy Labels for Sentence-level Sentiment Classification

Hao Wang, Bing Liu, Chaozhuo Li, Yan Yang, Tianrui Li


Abstract
Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the model may perform poorly. This paper studies the problem of learning with noisy labels for sentence-level sentiment classification. We propose a novel DNN model called NetAb (as shorthand for convolutional neural Networks with Ab-networks) to handle noisy labels during training. NetAb consists of two convolutional neural networks, one with a noise transition layer for dealing with the input noisy labels and the other for predicting ‘clean’ labels. We train the two networks using their respective loss functions in a mutual reinforcement manner. Experimental results demonstrate the effectiveness of the proposed model.
Anthology ID:
D19-1655
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6286–6292
Language:
URL:
https://aclanthology.org/D19-1655
DOI:
10.18653/v1/D19-1655
Bibkey:
Cite (ACL):
Hao Wang, Bing Liu, Chaozhuo Li, Yan Yang, and Tianrui Li. 2019. Learning with Noisy Labels for Sentence-level Sentiment Classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6286–6292, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Learning with Noisy Labels for Sentence-level Sentiment Classification (Wang et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1655.pdf