Title:
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A KNOWLEDGE BASED TWO-STAGE CASCADE MODEL
FOR TEST-QUESTION/ANSWER RETRIEVAL |
Author(s):
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Yilu Wei,Daifeng Li and Andrew David Madden |
ISBN:
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978-989-8533-92-0 |
Editors:
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Ajith P. Abraham and Jörg Roth |
Year:
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2019 |
Edition:
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Single |
Keywords:
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Deep Learning, Test-Question/Answer Retrieval, Knowledge Feature, Cascade Model |
Type:
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Full Paper |
First Page:
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23 |
Last Page:
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30 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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Test-question/answer retrieval task has raised higher requirements in terms of accuracy, coverage and semantic
understanding. We design a cascade model with two-stage training processes: The first stage uses 41,532 user test-question
click records and 207,660 unclick records, which are collected from a designed test-question-answer experimental platform,
to generate 200,000 pairwise training dataset to train a deep learning model, which could improve generalization ability.
The second stage combines the output of the first stage with structural knowledge as new features to train a logistic
regression for selecting the results from the candidates with higher accuracy, the training dataset is generated by manually
annotating 20,000 test-question samples. The structural knowledge is also manually extracted from the samples for
generating a small knowledge graph, and on this condition, we design knowledge features. Experimental results show that
the proposed model outperforms the state-of-the-art algorithms, among which the cascading model contributes 3%
improvement and the knowledge features contribute 1% improvement. |
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