Abstract
In this chapter, we describe our question answering system, which was the winning system at the Human–Computer Question Answering (HCQA) Competition at the Thirty-first Annual Conference on Neural Information Processing Systems (NIPS). The competition requires participants to address a factoid question answering task referred to as quiz bowl. To address this task, we use two novel neural network models and combine these models with conventional information retrieval models using a supervised machine learning model. Our system achieved the best performance among the systems submitted in the competition and won a match against six top human quiz experts by a wide margin.
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Notes
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The dataset was obtained from the authors’ website: https://cs.umd.edu/~miyyer/qblearn/.
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The mapping was obtained from FIGER’s GitHub repository: https://github.com/xiaoling/figer/.
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We aggregate probabilities because an entity can have multiple entity types in both the coarse-grained and the fine-grained models.
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We use the list of stop words contained in the scikit-learn library.
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We use Apache OpenNLP to detect noun words and proper noun words.
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Yamada, I., Tamaki, R., Shindo, H., Takefuji, Y. (2018). Studio Ousia’s Quiz Bowl Question Answering System. In: Escalera, S., Weimer, M. (eds) The NIPS '17 Competition: Building Intelligent Systems. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-94042-7_10
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