@inproceedings{wang-etal-2019-learning-noisy,
title = "Learning with Noisy Labels for Sentence-level Sentiment Classification",
author = "Wang, Hao and
Liu, Bing and
Li, Chaozhuo and
Yang, Yan and
Li, Tianrui",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1655",
doi = "10.18653/v1/D19-1655",
pages = "6286--6292",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Learning with Noisy Labels for Sentence-level Sentiment Classification
%A Wang, Hao
%A Liu, Bing
%A Li, Chaozhuo
%A Yang, Yan
%A Li, Tianrui
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wang-etal-2019-learning-noisy
%X 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.
%R 10.18653/v1/D19-1655
%U https://aclanthology.org/D19-1655
%U https://doi.org/10.18653/v1/D19-1655
%P 6286-6292
Markdown (Informal)
[Learning with Noisy Labels for Sentence-level Sentiment Classification](https://aclanthology.org/D19-1655) (Wang et al., EMNLP-IJCNLP 2019)
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.