Abstract
Chinese character puzzles are popular games in China. To solve a character puzzle, people need to fully consider the meaning and the strokes of each character in puzzles. Therefore, Chinese character puzzles are complicated and it can be a challenging task in natural language processing. In this paper, we collect a Chinese character puzzles dataset (CCPD) and design a Stroke Sensitive Character Guessing (SSCG) Model. SSCG can consider the meaning and strokes of each character. In this way, SSCG can solve Chinese character puzzles more accurately. To the best of our knowledge, it is the first work which tries to handle the Chinese character puzzles. We evaluate SSCG on CCPD. The experiment results show the effectiveness of the SSCG.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings [1]
Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Proceedings of SSST@EMNLP 2014, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 25 October 2014, pp. 103–111 (2014)
Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 8–13 December 2014, Montreal, Quebec, Canada, pp. 2042–2050 (2014)
Ji, Z., Lu, Z., Li, H.: An information retrieval approach to short text conversation. CoRR abs/1408.6988 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)
Lowe, R., Pow, N., Serban, I., Pineau, J.: The ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. In: Proceedings of the SIGDIAL 2015 Conference, The 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Prague, Czech Republic, 2–4 September 2015, pp. 285–294 (2015)
Ren, D., Cai, Y., Lei, X., Xu, J., Li, Q., Leung, H.: A multi-encoder neural conversation model. Neurocomputing 358, 344–354 (2019). https://doi.org/10.1016/j.neucom.2019.05.071
Severyn, A., Moschitti, A.: Automatic feature engineering for answer selection and extraction. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, Grand Hyatt Seattle, Seattle, Washington, USA, 18–21 October 2013, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 458–467 (2013)
Shao, Y., Gouws, S., Britz, D., Goldie, A., Strope, B., Kurzweil, R.: Generating high-quality and informative conversation responses with sequence-to-sequence models. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9–11 September 2017, pp. 2210–2219 (2017)
Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA, 4–9 December 2017, pp. 6000–6010 (2017)
Wang, H., Lu, Z., Li, H., Chen, E.: A dataset for research on short-text conversations. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, Grand Hyatt Seattle, Seattle, Washington, USA, 18–21 October 2013, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 935–945 (2013)
Wang, M., Smith, N.A., Mitamura, T.: What is the jeopardy model? A quasi-synchronous grammar for QA. In: EMNLP-CoNLL 2007, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, Czech Republic, 28–30 June 2007, pp. 22–32 (2007)
Wang, M., Lu, Z., Li, H., Liu, Q.: Syntax-based deep matching of short texts. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, 25–31 July 2015, pp. 1354–1361 (2015)
Wu, W., Sun, X., Wang, H.: Question condensing networks for answer selection in community question answering. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15–20 July 2018, vol. 1, Long Papers, pp. 1746–1755 (2018)
Wu, W., Wang, H., Li, S.: Bi-directional gated memory networks for answer selection. In: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017, and - 5th International Symposium, NLP-NABD 2017, Nanjing, China, 13–15 October 2017, Proceedings, pp. 251–262 (2017). https://doi.org/10.1007/978-3-319-69005-6_21
Wu, Y., Li, Z., Wu, W., Zhou, M.: Response selection with topic clues for retrieval-based chatbots. Neurocomputing 316, 251–261 (2018). https://doi.org/10.1016/j.neucom.2018.07.073
Wu, Y., Wei, F., Huang, S., Wang, Y., Li, Z., Zhou, M.: Response generation by context-aware prototype editing. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, 27 January–1 February 2019, pp. 7281–7288 (2019)
Wu, Y., Wu, W., Xing, C., Zhou, M., Li, Z.: Sequential matching network: a new architecture for multi-turn response selection in retrieval-based chatbots. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July–4 August, vol. 1, Long Papers, pp. 496–505 (2017). https://doi.org/10.18653/v1/P17-1046
Yan, R., Song, Y., Wu, H.: Learning to respond with deep neural networks for retrieval-based human-computer conversation system. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, 17–21 July 2016, pp. 55–64 (2016). https://doi.org/10.1145/2911451.2911542
Yao, K., Peng, B., Zweig, G., Wong, K.: An attentional neural conversation model with improved specificity. CoRR abs/1606.01292 (2016)
Yih, W., Chang, M., Meek, C., Pastusiak, A.: Question answering using enhanced lexical semantic models. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, Sofia, Bulgaria, 4–9 August 2013, vol. 1, Long Papers, pp. 1744–1753 (2013)
Young, T., Cambria, E., Chaturvedi, I., Zhou, H., Biswas, S., Huang, M.: Augmenting end-to-end dialogue systems with commonsense knowledge (2018)
Zhang, Z., Li, J., Zhu, P., Zhao, H., Liu, G.: Modeling multi-turn conversation with deep utterance aggregation. In: Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, 20–26 August 2018, pp. 3740–3752 (2018)
Zhou, X., Dong, D., Wu, H., Zhao, S., Yu, D., Tian, H., Liu, X., Yan, R.: Multi-view response selection for human-computer conversation. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, 1–4 November 2016, pp. 372–381 (2016)
Zhou, X., et al.: Multi-turn response selection for chatbots with deep attention matching network. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15–20 July 2018, vol. 1, Long Papers, pp. 1118–1127 (2018)
Acknowledgment
This work presented in this paper is partially supported by the Fundamental Research Funds for the Central Universities, SCUT (Nos. 2017ZD048, D2182480), the Tiptop Scientific and Technical Innovative Youth Talents of Guangdong special support program (No.2015TQ01X633), the Science and Technology Planning Project of Guangdong Province (No.2017B050506004), the Science and Technology Program of Guangzhou (Nos. 201704030076, 201802010027). The research described in this paper has been supported by a collaborative research grant from the Hong Kong Research Grants Council (project no. C1031-18G).
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Ren, D., Cai, Y., Li, W., Xia, R., Li, Z., Li, Q. (2019). Solving Chinese Character Puzzles Based on Character Strokes. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_24
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DOI: https://doi.org/10.1007/978-3-030-32233-5_24
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