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        <title>Generating Questions for Reading Comprehension using Coherence Relations</title>
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        <namePart type="given">Takshak</namePart>
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        <name type="personal">
            <namePart type="given">Yuen-Hsien</namePart>
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    <abstract>In this paper, we have proposed a technique for generating complex reading comprehension questions from a discourse that are more useful than factual ones derived from assertions. Our system produces a set of general-level questions using coherence relations and a set of well-defined syntactic transformations on the input text. Generated questions evaluate comprehension abilities like a comprehensive analysis of the text and its structure, correct identification of the author’s intent, a thorough evaluation of stated arguments; and a deduction of the high-level semantic relations that hold between text spans. Experiments performed on the RST-DT corpus allow us to conclude that our system possesses a strong aptitude for generating intricate questions. These questions are capable of effectively assessing a student’s interpretation of the text.</abstract>
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%0 Conference Proceedings
%T Generating Questions for Reading Comprehension using Coherence Relations
%A Desai, Takshak
%A Dakle, Parag
%A Moldovan, Dan
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Ng, Vincent
%Y Komachi, Mamoru
%S Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F desai-etal-2018-generating
%X In this paper, we have proposed a technique for generating complex reading comprehension questions from a discourse that are more useful than factual ones derived from assertions. Our system produces a set of general-level questions using coherence relations and a set of well-defined syntactic transformations on the input text. Generated questions evaluate comprehension abilities like a comprehensive analysis of the text and its structure, correct identification of the author’s intent, a thorough evaluation of stated arguments; and a deduction of the high-level semantic relations that hold between text spans. Experiments performed on the RST-DT corpus allow us to conclude that our system possesses a strong aptitude for generating intricate questions. These questions are capable of effectively assessing a student’s interpretation of the text.
%R 10.18653/v1/W18-3701
%U https://aclanthology.org/W18-3701/
%U https://doi.org/10.18653/v1/W18-3701
%P 1-10

Markdown (Informal)

[Generating Questions for Reading Comprehension using Coherence Relations](https://aclanthology.org/W18-3701/) (Desai et al., NLP-TEA 2018)

• Generating Questions for Reading Comprehension using Coherence Relations (Desai et al., NLP-TEA 2018)

ACL

• Takshak Desai, Parag Dakle, and Dan Moldovan. 2018. Generating Questions for Reading Comprehension using Coherence Relations. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 1–10, Melbourne, Australia. Association for Computational Linguistics.