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Author: Avi Bleiweiss

Affiliation: BShalem Research, Sunnyvale and U.S.A.

Keyword(s): Machine Comprehension, Gated Recurrent Neural Networks, Coattention Mechanism, Passage Ranking.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Context Discovery ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining Text and Semi-Structured Data ; Soft Computing ; Symbolic Systems

Abstract: Machine comprehension has gained increased interest with the recent release of real-world and large-scale datasets. In this work, we developed a neural model built of multiple coattention encoders to address datasets that draw answers to a query from orthogonal context passages. The novelty of our model is in producing passage ranking based entirely on the answer quality obtained from coattention processing. We show that using instead the search-engine presentation order of indexed web pages, from which evidence articles have been extracted, may affect performance adversely. To evaluate our model, we chose the MSMARCO dataset that allows queries to have anywhere from no answer to multiple answers assembled of words both in and out of context. We report extensive quantitative results to show performance impact of various dataset components.

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Paper citation in several formats:
Bleiweiss, A. (2018). Ranking Quality of Answers Drawn from Independent Evident Passages in Neural Machine Comprehension. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KDIR; ISBN 978-989-758-330-8; ISSN 2184-3228, SciTePress, pages 61-71. DOI: 10.5220/0006923300610071

@conference{kdir18,
author={Avi Bleiweiss},
title={Ranking Quality of Answers Drawn from Independent Evident Passages in Neural Machine Comprehension},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KDIR},
year={2018},
pages={61-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006923300610071},
isbn={978-989-758-330-8},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KDIR
TI - Ranking Quality of Answers Drawn from Independent Evident Passages in Neural Machine Comprehension
SN - 978-989-758-330-8
IS - 2184-3228
AU - Bleiweiss, A.
PY - 2018
SP - 61
EP - 71
DO - 10.5220/0006923300610071
PB - SciTePress

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