Abstract
Deep learning solutions have been widely used lately for improving question answering systems, especially as the amount of training data has increased. However, these solutions have been developed for specific tasks, when both the question and the candidate answers are long enough for the deep learning models to provide a better text representation and a more complex similarity function. For multiple choice questions that have short answers, information retrieval solutions are still largely used. In this paper we propose a novel deep learning model that determines the correct answer by combining the representation of each question-candidate answer pair with candidate contexts extracted from Wikipedia using a search engine.
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More info online at The Allen AI Science Challenge, https://www.kaggle.com/c/the-allen-ai-science-challenge.
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Nicula, B., Ruseti, S., Rebedea, T. (2018). Improving Deep Learning for Multiple Choice Question Answering with Candidate Contexts. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_62
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DOI: https://doi.org/10.1007/978-3-319-76941-7_62
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