Abstract
We demonstrate significant improvement on the MCTest question answering task (Richardson et al., 2013) by augmenting baseline features with features based on syntax, frame semantics, coreference, and word embeddings, and combining them in a max-margin learning framework. We achieve the best results we are aware of on this dataset, outperforming concurrentlypublished results. These results demonstrate a significant performance gradient for the use of linguistic structure in machine comprehension.
Cite
CITATION STYLE
Wang, H., Bansal, M., Gimpel, K., & McAllester, D. (2015). Machine comprehension with syntax, frames, and semantics. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (Vol. 2, pp. 700–706). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p15-2115
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