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Towards Generating Deep Questions from Text.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards Generating Deep Questions from Text./
作者:	Pan, Liangming.
面頁冊數:	1 online resource (156 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
Contained By:	Dissertations Abstracts International84-04B.
標題:	Tutoring. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29352295click for full text (PQDT)
ISBN:	9798352682890

Towards Generating Deep Questions from Text.
Pan, Liangming.

Towards Generating Deep Questions from Text. - 1 online resource (156 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: B.

Thesis (Ph.D.)--National University of Singapore (Singapore), 2022.

Includes bibliographical references

Question Generation (QG) concerns the task of automatically generating questions from various inputs such as raw text, database, or semantic representation. People have the ability to ask deep questions about events, evaluation, opinions, synthesis, or reasons, usually in the form of Why, Why-not, How, What-if, which requires an in-depth understanding of the input source and the ability to reason over disjoint relevant contexts. Learning to ask such deep questions has broad application in future intelligent systems, such as dialog systems, online education, and intelligent search, among others.In this thesis, we conduct an in-depth study of Deep Question Generation (DQG): the task of generating deep questions that demand high cognitive skills, especially questions that require multi-hop reasoning over different texts. Specifically, we focus on two fundamental research questions: 1) how to effectively generate deep questions from text, and 2) how deep question generation benefits real-world NLP applications.To explore the first research question, we propose three methods that focus on different challenges of DQG: 1) facilitating the document-level understanding in DQG by building semantic graph representations of the input text; 2) incorporating deep reasoning skills into DQG by explicitly modeling typical reasoning patterns of human question-asking; and 3) generating deep questions with certain human-desired properties via reinforcement learning.To study the second research question, we explore two real-world applications: multi-hop question answering and automated fact-checking. We create synthetic training data for these two tasks with the help of question generation. We show that DQG can greatly benefit the above tasks in terms of boosting performance and reducing human annotations.Finally, we summarize the strengths, weaknesses, and implications of our work, and discuss the future research plan of generalizing our work to a wider range of deep questions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352682890Subjects--Topical Terms:

3682198
Tutoring.
Index Terms--Genre/Form:

542853
Electronic books.

Towards Generating Deep Questions from Text.
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504 $a Includes bibliographical references
520 $a Question Generation (QG) concerns the task of automatically generating questions from various inputs such as raw text, database, or semantic representation. People have the ability to ask deep questions about events, evaluation, opinions, synthesis, or reasons, usually in the form of Why, Why-not, How, What-if, which requires an in-depth understanding of the input source and the ability to reason over disjoint relevant contexts. Learning to ask such deep questions has broad application in future intelligent systems, such as dialog systems, online education, and intelligent search, among others.In this thesis, we conduct an in-depth study of Deep Question Generation (DQG): the task of generating deep questions that demand high cognitive skills, especially questions that require multi-hop reasoning over different texts. Specifically, we focus on two fundamental research questions: 1) how to effectively generate deep questions from text, and 2) how deep question generation benefits real-world NLP applications.To explore the first research question, we propose three methods that focus on different challenges of DQG: 1) facilitating the document-level understanding in DQG by building semantic graph representations of the input text; 2) incorporating deep reasoning skills into DQG by explicitly modeling typical reasoning patterns of human question-asking; and 3) generating deep questions with certain human-desired properties via reinforcement learning.To study the second research question, we explore two real-world applications: multi-hop question answering and automated fact-checking. We create synthetic training data for these two tasks with the help of question generation. We show that DQG can greatly benefit the above tasks in terms of boosting performance and reducing human annotations.Finally, we summarize the strengths, weaknesses, and implications of our work, and discuss the future research plan of generalizing our work to a wider range of deep questions.
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