Abstract
In this paper, we focus on the problem of answer triggering addressed by Yang et al. (2015), which is a critical component for a real-world question answering system. We employ a hierarchical gated recurrent neural tensor (HGRNT) model to capture both the context information and the deep interactions between the candidate answers and the question. Our result on F value achieves 42.6%, which surpasses the baseline by over 10 %.
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We re-implement the model as the paper described, but we were not able to get as good as the original MRR and MAP result they claim. But this is not the focus of our paper.
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References
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Acknowledgement
This work is supported by the National Key Basic Research Program of China (2014CB340504), the National Natural Science Foundation of China (61371129, 61572245).
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Li, W., Wu, Y. (2017). Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_24
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DOI: https://doi.org/10.1007/978-3-319-69005-6_24
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