Question Difficulty Estimation Based on Attention Model for Question Answering
This paper addresses a question difficulty estimation of which goal is to estimate the difficulty level of a given question in question-answering (QA) tasks. Since a question in the tasks is composed of a questionary sentence and a set of information components such as a description and candidate an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/24/12023 |
| _version_ | 1827674203937046528 |
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| author | Hyun-Je Song Su-Hwan Yoon Seong-Bae Park |
| author_facet | Hyun-Je Song Su-Hwan Yoon Seong-Bae Park |
| author_sort | Hyun-Je Song |
| collection | DOAJ |
| description | This paper addresses a question difficulty estimation of which goal is to estimate the difficulty level of a given question in question-answering (QA) tasks. Since a question in the tasks is composed of a questionary sentence and a set of information components such as a description and candidate answers, it is important to model the relationship among the information components to estimate the difficulty level of the question. However, existing approaches to this task modeled a simple relationship such as a relationship between a questionary sentence and a description, but such simple relationships are insufficient to predict the difficulty level accurately. Therefore, this paper proposes an attention-based model to consider the complicated relationship among the information components. The proposed model first represents bi-directional relationships between a questionary sentence and each information component using a dual multi-head co-attention, since the questionary sentence is a key factor in the QA questions and it affects and is affected by information components. Then, the proposed model considers inter-information relationship over the bi-directional representations through a self-attention model. The inter-information relationship helps predict the difficulty of the questions accurately which require reasoning over multiple kinds of information components. The experimental results from three well-known and real-world QA data sets prove that the proposed model outperforms the previous state-of-the-art and pre-trained language model baselines. It is also shown that the proposed model is robust against the increase of the number of information components. |
| first_indexed | 2024-03-10T04:36:18Z |
| format | Article |
| id | doaj.art-0635d9a3cd634d829dbc18b175114668 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:36:18Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0635d9a3cd634d829dbc18b1751146682023-11-23T03:41:59ZengMDPI AGApplied Sciences2076-34172021-12-0111241202310.3390/app112412023Question Difficulty Estimation Based on Attention Model for Question AnsweringHyun-Je Song0Su-Hwan Yoon1Seong-Bae Park2Department of Information Technology, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Youngin 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Youngin 17104, KoreaThis paper addresses a question difficulty estimation of which goal is to estimate the difficulty level of a given question in question-answering (QA) tasks. Since a question in the tasks is composed of a questionary sentence and a set of information components such as a description and candidate answers, it is important to model the relationship among the information components to estimate the difficulty level of the question. However, existing approaches to this task modeled a simple relationship such as a relationship between a questionary sentence and a description, but such simple relationships are insufficient to predict the difficulty level accurately. Therefore, this paper proposes an attention-based model to consider the complicated relationship among the information components. The proposed model first represents bi-directional relationships between a questionary sentence and each information component using a dual multi-head co-attention, since the questionary sentence is a key factor in the QA questions and it affects and is affected by information components. Then, the proposed model considers inter-information relationship over the bi-directional representations through a self-attention model. The inter-information relationship helps predict the difficulty of the questions accurately which require reasoning over multiple kinds of information components. The experimental results from three well-known and real-world QA data sets prove that the proposed model outperforms the previous state-of-the-art and pre-trained language model baselines. It is also shown that the proposed model is robust against the increase of the number of information components.https://www.mdpi.com/2076-3417/11/24/12023attention modeldual multi-head attentioninter-information relationshipquestion answeringquestion difficult estimation |
| spellingShingle | Hyun-Je Song Su-Hwan Yoon Seong-Bae Park Question Difficulty Estimation Based on Attention Model for Question Answering Applied Sciences attention model dual multi-head attention inter-information relationship question answering question difficult estimation |
| title | Question Difficulty Estimation Based on Attention Model for Question Answering |
| title_full | Question Difficulty Estimation Based on Attention Model for Question Answering |
| title_fullStr | Question Difficulty Estimation Based on Attention Model for Question Answering |
| title_full_unstemmed | Question Difficulty Estimation Based on Attention Model for Question Answering |
| title_short | Question Difficulty Estimation Based on Attention Model for Question Answering |
| title_sort | question difficulty estimation based on attention model for question answering |
| topic | attention model dual multi-head attention inter-information relationship question answering question difficult estimation |
| url | https://www.mdpi.com/2076-3417/11/24/12023 |
| work_keys_str_mv | AT hyunjesong questiondifficultyestimationbasedonattentionmodelforquestionanswering AT suhwanyoon questiondifficultyestimationbasedonattentionmodelforquestionanswering AT seongbaepark questiondifficultyestimationbasedonattentionmodelforquestionanswering |