Abstract
Mongolian word segmentation is splitting the Mongolian words into roots and suffixes. It plays an important role in Mongolian related natural language processing tasks. To improve performance and avoid the tedious work of rule-making and statistics over large-scale corpus in early methods, this work takes a Seq2Seq framework to realize Mongolian word segmentation. Since each Mongolian word consisted of several sequential characters, we map Mongolian word segmentation to character-level Seq2Seq task, and further propose three different models from three different prospective to achieve the segmentation goal. The three character-level Seq2Seq models are (1) translation model, (2) true and pseudo mapping model, (3) binary choice model. The main differences of these three models are the output sequences and the architectures of the RNNs in segmentation. We employ an improved beam search to optimize the second segmentation model and boost the segmentation process. All the models are trained on a limited dataset, and the second model achieved the state-of-the-art accuracy.
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References
Hankamer, J.: Finite state morphology and left to right phonology. In: Proceedings of the West Coast Conference on Formal Linguistics, vol. 5, pp. 41–52 (1986)
Na, L., Junyi, W., Guiping, L.: Query expansion based on mongolian semantics. In: Third World Congress on Software Engineering IEEE Computer Society, pp. 25–28. IEEE, Wuhan (2012)
Jing, W., Hou, H., Bao, F., Jiang, Y.: Template-based model for Mongolian - Chinese machine translation. In: Technologies and Applications of Artificial Intelligence, pp. 352–357. IEEE, Tainan (2016)
Weihua, W., Feilong, B., Guanglai, G.: Mongolian named entity recognition with bidirectional recurrent neural networks. In: IEEE International Conference on TOOLS with Artificial Intelligence, pp. 495–500. IEEE, San Jose (2017)
Liu, R., Bao, F., Gao, G., Wang, Y.: Mongolian text-to-speech system based on deep neural network. In: Tao, J., Zheng, T.F., Bao, C., Wang, D., Li, Y. (eds.) NCMMSC 2017. CCIS, vol. 807, pp. 99–108. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8111-8_10
Hongxu, H., Liu, Q., Nasanurtu, M.: Mongolian word segmentation based on statistical language model. Pattern Recognit. Artif. Intell. 22(1), 108–112 (2009)
Ming, Y., Hongxu, H.: Researching of Mongolian word segmentation system based on dictionary, rules and language model. M.S. Thesis, Inner Mongolia University, Hohhot, Inner Mongolia, China (2011)
Jianguo, S., Hongxu, H., Bao, F.: Research on Slavic Mongolian word segmentation based on dictionary and rule. J. Chin. Inf. Process. 29(1), 197–202 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pp. 1412–1421 (2015)
Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Vaswani, A., et al.: Tensor2tensor for neural machine translation. arXiv preprint arXiv:1803.07416 (2018)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Computer Science. arXiv preprint arXiv:1409.0473(2014)
Shiqi, S., Yong, C., Zhongjun, H., Wei, H., et al.: Minimum risk training for neural machine translation. In: ACL 2016: Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1683–1692. ACL, Berlin (2016)
Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., Bengio, Y.: End-to-end attention-based large vocabulary speech recognition. Computer Science, pp. 4945–4949 (2016)
Arık, S.O., Chrzanowski, M., Coates, A., Diamos, G., Gibiansky, A.: Deep voice: realtime neural text-to-speech. In: International Conference on Machine Learning and Computing, ICMLC 2017, pp. 195–2049. ACM, Singapore (2017)
Asri, L.E., He, J., Suleman, K.: A sequence-to-sequence model for user simulation in spoken dialogue systems. In: Conference of the International Speech Communication Association, Interspeech, pp. 1151–1155. IEEE, San Francisco (2016)
Nallapati, R., Zhou, B., Santos, C., Gulcehre, C., Xiang, B.: Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, pp. 280–290. ACL, Berlin (2016)
See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. In: ACL 2017: Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1073–1083. ACL, Vancouver (2017)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems 27 (NIPS 2014), vol. 4, pp. 3104–3112. MIT Press Cambridge, Montréal (2014)
Hakkani-Tür, D., Tur, G., Celikyilmaz, A., Chen, Y.N., Gao, J., Deng, L.: Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM. In: Conference of the International Speech Communication Association, Interspeech, pp. 715–719. IEEE, San Francisco (2016)
Mnih, V., Heess, N., Graves, A., Kavukcuoglu, K.: Recurrent models of visual attention. In: Advances in Neural Information Processing Systems 27 (NIPS 2014), vol. 3, pp. 2204–2212. MIT Press Cambridge, Montréal (2014)
Neubig, G.: Neural machine translation and sequence-to-sequence models: a tutorial, pp. 41–43. arXiv preprint arXiv: 1703.01619v1 (2017)
Acknowledgements
This work was funded by National Natural Science Foundation of China (Grant No. 61762069), Natural Science Foundation of Inner Mongolia Autonomous Region (Grant No. 2017BS0601), Research program of science and technology at Universities of Inner Mongolia Autonomous Region (Grant No. NJZY18237).
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Liu, N., Su, X., Gao, G., Bao, F. (2018). Mongolian Word Segmentation Based on Three Character Level Seq2Seq Models. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_50
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