Detection of Similar Answers to Avoid Duplicate Question in Retrieval-based Automatic Question Generation


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 1, pp. 27-36, Jan. 2019
https://doi.org/10.3745/KTSDE.2019.8.1.27,   PDF Download:
Keywords: Question-Answer Database, Automatic Question Generation, Similar Answer Detection
Abstract

In this paper, we propose a method to find the most similar answer to the user‘s response from the question-answer database in order to avoid generating a redundant question in retrieval-based automatic question generation system. As a question of the most similar answer to user’s response may already be known to the user, the question should be removed from a set of question candidates. A similarity detector calculates a similarity between two answers by utilizing the same words, paraphrases, and sentential meanings. Paraphrases can be acquired by building a phrase table used in a statistical machine translation. A sentential meaning’s similarity of two answers is calculated by an attention-based convolutional neural network. We evaluate the accuracy of the similarity detector on an evaluation set with 100 answers, and can get the 71% Mean Reciprocal Rank (MRR) score.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
Y. Choi and K. J. Lee, "Detection of Similar Answers to Avoid Duplicate Question in Retrieval-based Automatic Question Generation," KIPS Transactions on Software and Data Engineering, vol. 8, no. 1, pp. 27-36, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.1.27.

[ACM Style]
Yong-Seok Choi and Kong Joo Lee. 2019. Detection of Similar Answers to Avoid Duplicate Question in Retrieval-based Automatic Question Generation. KIPS Transactions on Software and Data Engineering, 8, 1, (2019), 27-36. DOI: https://doi.org/10.3745/KTSDE.2019.8.1.27.