深度学习已经在许多自然语言处理任务中达到了最高水平的性能,但是这种方法通常需要大量的标注数据。该文围绕问题意图识别语料标注问题,结合深度学习和主动学习技术,实现了语料标注成本的降低。主动学习需要不断迭代地再训练,计算成本非常高,为加速这个进程,该文提出了一种适合问题意图识别任务的轻量级架构,使用双层CNN结构组成的深度学习模型。同时为更好地评估样本的价值,设计了一种结合样本的信息性、代表性与多样性的多准则主动学习方法。最终在民航客服语料下进行实验,实验结果表明该方法可减少约50%的标注工作量,同时在公开数据集TREC问题分类语料上验证了该方法的通用性。
Abstract
Deep learning has achieved best performance in many natural language processing tasks on the basis of large amount of annotation data. To reduce the cost of corpus annotation, this paper combines the active learning and deep learning to identify the corpus of question intent. To minimize the iteration of retraining in active learning, a lightweight architecture suitable for question intent recognition task is proposed by using a deep learning model consisting of a two-layer CNN structure. At the same time, in order to better evaluate the value of the sample, a multi-criteria active learning method is designed by considering the information, representativeness and diversity of samples. Finally, experiments on the civil aviation customer service corpus show that the method can reduce the annotation workload by about 50%, which is also validated by the public dataset TREC question classification corpus.
关键词
主动学习 /
文本标注 /
意图识别
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Key words
active learning /
text annotation /
intention recognition
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参考文献
[1] Young T, Hazarika D,Poria S, et al. Recent trends in deep learning based natural language processing[J]. IEEE Computational Intelligence Magazine, 2018, 13(3): 55-75.
[2] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature,2015,521(7553): 436-444.
[3] Yang L, Zhang Y, Chen J, et al. Suggestive annotation: A deep active learning framework for biomedical image segmentation[C]//Proceedings of Medical Image Computing and Computer Assisted Intervention, 2017: 399-407.
[4] Yang Y, Ma Z,Nie F, et al. Multi-class active learning by uncertainty sampling with diversity maximization[J]. International Journal of Computer Vision,2015,113(2): 113-127.
[5] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of Advances in Neural Information Processing Systems,2013,26: 3111-3119.
[6] Joulin A, Grave E, Bojanowski P, et al. Bag of tricks for efficient text classification[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics,2017: 427-431.
[7] Kim Y. Convolutional neural networks for sentence classification[C]//Proceedings of EMNLP 2014,2014: 1746-1751.
[8] Liu P,Qiu X, Huang X, et al. Recurrent neural network for text classification with multi-task learning[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence,2016: 2873-2879.
[9] Zhou C, Sun C, Liu Z, et al. A C-LSTM neural network for text classification[J]. Computer Science,2015,1(4): 39-44.
[10] Chung J,Gulcehre C, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[C]//Proceedings of NIPS 2014 Workshop on Deep Learning,2014.
[11] Lai S, Xu L, Liu K, et al. Recurrent convolutional neural networks for text classification[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence,2015:2267-2273.
[12] Vaswani A,Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of Advances in Neural Information Processing Systems, 2017: 5998-6008.
[13] Devlin J, Chang M, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of NAACL 2019, 2019: 4171-4186.
[14] Kang L, Xu Q,Ziqiang W, et al. Survey on active learning algorithms[J]. Computer Engineering and Applications, 2012, 48(34): 1-4.
[15] Zhu J, Wang H, Yao T, et al. Active learning with sampling by uncertainty and density for word sense disambiguation and text classification[C]//Proceedings of COLING 2008,2008: 1137-1144.
[16] Settles B, Craven M. An analysis of active learning strategies for sequence labeling tasks[C]//Proceedings of EMNLP 2008,2008: 1070-1079.
[17] Rubens N, Elahi M, Sugiyama M, et al. Active learning in recommender systems[M]. Recommender Systems Handbook: Springer,2016: 809-846.
[18] Krizhevsky A, Sutskever I, Hinton G E, et al. ImageNet classification with deep convolutional neural networks[J].Neural Information Processing Systems,2012,141(5): 1097-1105.
[19] Hinton G E, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups[J]. IEEE Signal Processing Magazine, 2012,29(6): 82-97.
[20] Wang K, Zhang D, Li Y, et al. Cost-effective active learning for deep image classification[J]. IEEE Transactions on Circuits and Systems for Video Technology,2017,27(12): 2591-2600.
[21] Zhou Z, Shin J, Zhang L, et al. Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally[C]//Proceedings of Computer Vision and Pattern Recognition,2017: 4761-4772.
[22] Zhang Y, Lease M, Wallace B C, et al. Active discriminative text representation learning[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence,2017: 3386-3392.
[23] Shen Y, Yun H, Lipton Z C, et al. Deep active learning for named entity recognition[C]//Proceedings of the 2nd Workshop on Representation Learning for NLP,2017: 252-256.
[24] An B, Wu W, Han H. Deep active learning for text classification[C]//Proceedings of the 2nd International Conference on Vision, Image and Signal Processing, 2018: 1-6.
[25] Li X, Roth D. Learning question classifiers[C]//Proceedings of the 19th International Conference on Computational linguistics,2002: 1-7.
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脚注
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基金
国家重点研发计划(2017YFB1002303);国家自然科学基金(61802381,61972386);民航科技重大专项(MHRD20160109)
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