Question classification of CoQa (QCOC) dataset
This paper proposes a new dataset for question classification process. Named QCoC (Question Classification of CoQA), this dataset is created based on Stanford’s CoQA (A Conversational Question Answering Challenge) dataset. The total of QCoC datapoint is 116630 (total of combined questionanswer pairs...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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IEEE
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33107/ http://umpir.ump.edu.my/id/eprint/33107/1/Question%20classification%20of%20coqa_FULL.pdf http://umpir.ump.edu.my/id/eprint/33107/2/Question%20classification%20of%20coqa.pdf |
| _version_ | 1848824176143499264 |
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| author | Abbas Saliimi, Lokman Mohamed Ariff, Ameedeen Ngahzaifa, Ab. Ghani |
| author_facet | Abbas Saliimi, Lokman Mohamed Ariff, Ameedeen Ngahzaifa, Ab. Ghani |
| author_sort | Abbas Saliimi, Lokman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This paper proposes a new dataset for question classification process. Named QCoC (Question Classification of CoQA), this dataset is created based on Stanford’s CoQA (A Conversational Question Answering Challenge) dataset. The total of QCoC datapoint is 116630 (total of combined questionanswer pairs in CoQA training and evaluation dataset). Common question classification datasets are classifying question based on its paired answer’s knowledge (the semantic of answer’s context). For QCoC, classification is done differently that is per answer’s feature (semantic and syntactic of answer’s type). This paper discusses the question classification datasets, QA datasets, and justification of CoQA as selected base for QCoC. Then
QCoC specification is discussed with class definition, classification method and result subsections. To the author’s knowledge, such dataset is still nonexistent to date. This paper suggests that this type of dataset is useful in solving abstractive answers issue in Question-Answering (QA) system. While factual answers can be directly produced by regular QA system, abstractive answers need some additional components. Although it is a recognizable issue, lack of suitable dataset perhaps is the reason why such direction is not being pursued. With QCoC dataset made publicly available1, hopefully such direction is open for further exploration. |
| first_indexed | 2025-11-15T03:08:52Z |
| format | Conference or Workshop Item |
| id | ump-33107 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:08:52Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-331072022-06-22T01:57:52Z http://umpir.ump.edu.my/id/eprint/33107/ Question classification of CoQa (QCOC) dataset Abbas Saliimi, Lokman Mohamed Ariff, Ameedeen Ngahzaifa, Ab. Ghani QA76 Computer software This paper proposes a new dataset for question classification process. Named QCoC (Question Classification of CoQA), this dataset is created based on Stanford’s CoQA (A Conversational Question Answering Challenge) dataset. The total of QCoC datapoint is 116630 (total of combined questionanswer pairs in CoQA training and evaluation dataset). Common question classification datasets are classifying question based on its paired answer’s knowledge (the semantic of answer’s context). For QCoC, classification is done differently that is per answer’s feature (semantic and syntactic of answer’s type). This paper discusses the question classification datasets, QA datasets, and justification of CoQA as selected base for QCoC. Then QCoC specification is discussed with class definition, classification method and result subsections. To the author’s knowledge, such dataset is still nonexistent to date. This paper suggests that this type of dataset is useful in solving abstractive answers issue in Question-Answering (QA) system. While factual answers can be directly produced by regular QA system, abstractive answers need some additional components. Although it is a recognizable issue, lack of suitable dataset perhaps is the reason why such direction is not being pursued. With QCoC dataset made publicly available1, hopefully such direction is open for further exploration. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33107/1/Question%20classification%20of%20coqa_FULL.pdf pdf en http://umpir.ump.edu.my/id/eprint/33107/2/Question%20classification%20of%20coqa.pdf Abbas Saliimi, Lokman and Mohamed Ariff, Ameedeen and Ngahzaifa, Ab. Ghani (2021) Question classification of CoQa (QCOC) dataset. In: 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM) , 24-26 August 2021 , Pekan. pp. 1-2.. ISBN 978-166541407-4 (Published) http://10.1109/ICSECS52883.2021.00123 |
| spellingShingle | QA76 Computer software Abbas Saliimi, Lokman Mohamed Ariff, Ameedeen Ngahzaifa, Ab. Ghani Question classification of CoQa (QCOC) dataset |
| title | Question classification of CoQa (QCOC) dataset |
| title_full | Question classification of CoQa (QCOC) dataset |
| title_fullStr | Question classification of CoQa (QCOC) dataset |
| title_full_unstemmed | Question classification of CoQa (QCOC) dataset |
| title_short | Question classification of CoQa (QCOC) dataset |
| title_sort | question classification of coqa (qcoc) dataset |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/33107/ http://umpir.ump.edu.my/id/eprint/33107/ http://umpir.ump.edu.my/id/eprint/33107/1/Question%20classification%20of%20coqa_FULL.pdf http://umpir.ump.edu.my/id/eprint/33107/2/Question%20classification%20of%20coqa.pdf |