Abstract
Multi-task learning (MTL) models, which pool examples arisen out of several tasks, have achieved remarkable results in language processing. However, multi-task learning is not always effective when compared with the single-task methods in sequence tagging. One possible reason is that existing methods to multi-task sequence tagging often reply on lower layer parameter sharing to connect different tasks. The lack of interactions between different tasks results in limited performance improvement. In this paper, we propose a novel multi-task learning architecture which could iteratively utilize the prediction results of each task explicitly. We train our model for part-of-speech (POS) tagging, chunking and named entity recognition (NER) tasks simultaneously. Experimental results show that without any task-specific features, our model obtains the state-of-the-art performance on both chunking and NER.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China U1636103, 61632011, Shenzhen Foundational Research Funding 20170307150024907, Key Technologies Research and Development Program of Shenzhen JSGG20170817140856618.
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Gui, L., Du, J., Zhao, Z., He, Y., Xu, R., Fan, C. (2018). An End-to-End Scalable Iterative Sequence Tagging with Multi-Task Learning. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_25
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DOI: https://doi.org/10.1007/978-3-319-99501-4_25
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