Sequence to Sequence Model Performance for Education Chatbot.
Saved in:
| Title: | Sequence to Sequence Model Performance for Education Chatbot. |
|---|---|
| Authors: | Palasundram, Kulothunkan1, Sharef, Nurfadhlina Mohd1 nurfadhlina@upm.edu.my, Nasharuddin, Nurul Amelina1, Kasmiran, Khairul Azhar1, Azman, Azreen1 |
| Source: | International Journal of Emerging Technologies in Learning. 2019, Vol. 14 Issue 24, p56-68. 13p. |
| Subject Terms: | *School administrators, *Books & reading, *Educational planning, *Artificial intelligence, Chatbots, Recurrent neural networks |
| Abstract: | Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intelligence based. However, unlike the ruled-based chatbots, artificial intelligence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of the most commonly researched model to implement artificial intelligence chatbot and has shown great progress since its introduction in 2014. However, it is still in infancy and has not been applied widely in educational chatbot development. Introduced originally for neural machine translation, the Seq2Seq model has been adapted for conversation modelling including question-answering chatbots. However, indepth research and analysis of optimal settings of the various components of Seq2Seq model for natural answer generation problem is very limited. Additionally, there has been no experiments and analysis conducted to understand how Seq2Seq model handles variations is questions posed to it to generate correct answers. Our experiments add to the empirical evaluations on Seq2Seq literature and provides insights to these questions. Additionally, we provide insights on how a curated dataset can be developed and questions designed to train and test the performance of a Seq2Seq based question-answer model. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: ehh DbLabel: Education Research Complete An: 140495957 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Sequence to Sequence Model Performance for Education Chatbot. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Palasundram%2C+Kulothunkan%22">Palasundram, Kulothunkan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Sharef%2C+Nurfadhlina+Mohd%22">Sharef, Nurfadhlina Mohd</searchLink><relatesTo>1</relatesTo><i> nurfadhlina@upm.edu.my</i><br /><searchLink fieldCode="AR" term="%22Nasharuddin%2C+Nurul+Amelina%22">Nasharuddin, Nurul Amelina</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kasmiran%2C+Khairul+Azhar%22">Kasmiran, Khairul Azhar</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Azman%2C+Azreen%22">Azman, Azreen</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Emerging+Technologies+in+Learning%22">International Journal of Emerging Technologies in Learning</searchLink>. 2019, Vol. 14 Issue 24, p56-68. 13p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22School+administrators%22">School administrators</searchLink><br />*<searchLink fieldCode="DE" term="%22Books+%26+reading%22">Books & reading</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+planning%22">Educational planning</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intelligence based. However, unlike the ruled-based chatbots, artificial intelligence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of the most commonly researched model to implement artificial intelligence chatbot and has shown great progress since its introduction in 2014. However, it is still in infancy and has not been applied widely in educational chatbot development. Introduced originally for neural machine translation, the Seq2Seq model has been adapted for conversation modelling including question-answering chatbots. However, indepth research and analysis of optimal settings of the various components of Seq2Seq model for natural answer generation problem is very limited. Additionally, there has been no experiments and analysis conducted to understand how Seq2Seq model handles variations is questions posed to it to generate correct answers. Our experiments add to the empirical evaluations on Seq2Seq literature and provides insights to these questions. Additionally, we provide insights on how a curated dataset can be developed and questions designed to train and test the performance of a Seq2Seq based question-answer model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=140495957 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijet.v14i24.12187 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 56 Subjects: – SubjectFull: School administrators Type: general – SubjectFull: Books & reading Type: general – SubjectFull: Educational planning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Chatbots Type: general – SubjectFull: Recurrent neural networks Type: general Titles: – TitleFull: Sequence to Sequence Model Performance for Education Chatbot. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Palasundram, Kulothunkan – PersonEntity: Name: NameFull: Sharef, Nurfadhlina Mohd – PersonEntity: Name: NameFull: Nasharuddin, Nurul Amelina – PersonEntity: Name: NameFull: Kasmiran, Khairul Azhar – PersonEntity: Name: NameFull: Azman, Azreen IsPartOfRelationships: – BibEntity: Dates: – D: 27 M: 12 Text: 2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 18630383 Numbering: – Type: volume Value: 14 – Type: issue Value: 24 Titles: – TitleFull: International Journal of Emerging Technologies in Learning Type: main |
| ResultId | 1 |
