Abstract
With the recent development of sequence-to-sequence framework, generation approach for short text conversation becomes attractive. Traditional sequence-to-sequence method for short text conversation often suffers from dull response problem. Multi-resolution generation approach has been introduced to address this problem by dividing the generation process into two steps: keywords-sequence generation and response generation. However, this method still tends to generate short and dull keywords-sequence. In this work, a new multi-resolution generation framework is proposed. Instead of using the word-level maximum likelihood criterion, we optimize the sequence-level GLEU score of the entire generated keywords-sequence using a policy gradient approach in reinforcement learning. Experiments show that the proposed approach can generate longer and more diverse keywords-sequence. Meanwhile, it achieves better scores in the human evaluation.
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Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685 (2015)
Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. arXiv preprint arXiv:1503.02364 (2015)
Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:1506.05869 (2015)
Theis, L., van den Oord, A., Bethge, M.: A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844 (2015)
Mou, L., Song, Y., Yan, R., Li, G., Zhang, L., Jin, Z.: Sequence to backward and forward sequences: a content-introducing approach to generative short-text conversation. arXiv preprint arXiv:1607.00970 (2016)
Serban, I.V., Klinger, T., Tesauro, G., Talamadupula, K., Zhou, B., Bengio, Y., Courville, A.C.: Multiresolution recurrent neural networks: an application to dialogue response generation. In: AAAI, pp. 3288–3294 (2017)
Yu, L., Zhang, W., Wang, J., Yu, Y.: Seqgan: sequence generative adversarial nets with policy gradient. In: AAAI, pp. 2852–2858 (2017)
Sutton, R.S., McAllester, D.A., Singh, S.P., Mansour, Y.: Policy gradient methods for reinforcement learning with function approximation. In: Advances in Neural Information Processing Systems, pp. 1057–1063 (2000)
Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016)
Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Shang, L., Sakai, T., Lu, Z., Li, H., Higashinaka, R., Miyao, Y.: Overview of the NTCIR-12 short text conversation task. In: NTCIR (2016)
Che, W., Li, Z., Liu, T.: LTP: a Chinese language technology platform. In: Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pp. 13–16. Association for Computational Linguistics (2010)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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Liu, X., Yu, K. (2018). GLEU-Guided Multi-resolution Network for Short Text Conversation. In: Tao, J., Zheng, T., Bao, C., Wang, D., Li, Y. (eds) Man-Machine Speech Communication. NCMMSC 2017. Communications in Computer and Information Science, vol 807. Springer, Singapore. https://doi.org/10.1007/978-981-10-8111-8_2
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DOI: https://doi.org/10.1007/978-981-10-8111-8_2
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