| 摘要(英) |
Recently, Sequence Generative Adversarial Nets is very popular. It has achieved good results in both text generation and dialog generation. Therefore, we planned to build a question answering system via SeqGAN to teach machine reading question-related documents and generate answers like human responses. The demands for smart customer service is increasing day by day. Most of the systems built by template-based method failed to response users easily whenever the question asked by user does not follow the established rule or does not exist in the database, hence they are often limited on specific tasks. To resolve the weaknesses of the template-based customer service, we try to build a customer service with question answering system based on SeqGAN.
This thesis involves three parts. The first part is the pre-training of the generator and the discriminator. The generator is a seq2seq model with GRU. Besides, we use dynamic dictionary to decrease the probability that the decoder generates wrong words. The discriminator, which is a CNN model to classify the QA pair generated by human or machine, could give reward for both fully and partially QA pairs. The second part is SeqGAN. Different from general generative adversarial training, we consider the length difference between actual answer and generated answer when calculating the reward. Experimental results proved that the proposed system improved the performance successfully. In the last part we try to build a customer service system. We crawl the information such as product information and user manuals on the company website and use FAQ provided by the company to train our model. |
| 參考文獻 |
[1] UNGER, Christina, et al. Template-based question answering over RDF data. In: Proceedings of the 21st international conference on World Wide Web. ACM, 2012. p. 639-648.
[2] FENG, Minwei, et al. Applying deep learning to answer selection: A study and an open task. In: Automatic Speech Recognition and Understanding (ASRU), 2015 IEEE Workshop on. IEEE, 2015. p. 813-820.
[3] TAN, Ming, et al. LSTM-based deep learning models for non-factoid answer selection. arXiv preprint arXiv:1511.04108, 2015.
[4] MIKOLOV, Tomas, et al. Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems. 2013. p. 3111-3119.
[5] SUTSKEVER, Ilya; VINYALS, Oriol; LE, Quoc V. Sequence to sequence learning with neural networks. In: Advances in neural information processing systems. 2014. p. 3104-3112.
[6] YIN, Jun, et al. Neural generative question answering. arXiv preprint arXiv:1512.01337, 2015.
[7] MITRA, Rajarshee. An Abstractive approach to Question Answering. arXiv preprint arXiv:1711.06238, 2017.
[8] YU, Lantao, et al. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. In: AAAI. 2017. p. 2852-2858.
[9] LI, Jiwei, et al. Adversarial learning for neural dialogue generation. arXiv preprint arXiv:1701.06547, 2017.
[10] GUYEN, Tri, et al. MS MARCO: A human generated machine reading comprehension dataset. arXiv preprint arXiv:1611.09268, 2016.
[11] MNIH, Volodymyr, et al. Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning. 2016. p. 1928-1937.
[12] GOODFELLOW, Ian, et al. Generative adversarial nets. In: Advances in neural information processing systems. 2014. p. 2672-2680.
[13] LEDIG, Christian, et al. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint, 2016.
[14] SHRIVASTAVA, Ashish, et al. Learning from simulated and unsupervised images through adversarial training. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. p. 6.
[15] ZHU, Jun-Yan, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593, 2017.
[16] YU, Lantao, et al. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. In: AAAI. 2017. p. 2852-2858.
[17] LI, Jiwei, et al. Adversarial learning for neural dialogue generation. arXiv preprint arXiv:1701.06547, 2017. |
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