Sentiment-Driven Breakdown Detection Model Using Contextual Embedding ElMo

Almansor, EH; Hussain, FK

Sentiment-Driven Breakdown Detection Model Using Contextual Embedding ElMo

Almansor, EH Hussain, FK

Permalink

Publisher:: Springer
Publication Type:: Chapter
Citation:: Advanced Information Networking and Applications, 2021, 225, pp. 163-171
Issue Date:: 2021-01-01

Recently Added

	Filename	Description	Size
	Almansor-Hussain2021_Chapter_Sentiment-DrivenBreakdownDetec.pdf	Published version	504.42 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is new to OPUS and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Almansor, EH
dc.contributor.author	Hussain, FK
dc.date.accessioned	2021-08-24T02:07:56Z
dc.date.available	2021-08-24T02:07:56Z
dc.date.issued	2021-01-01
dc.identifier.citation	Advanced Information Networking and Applications, 2021, 225, pp. 163-171
dc.identifier.isbn	9783030750992
dc.identifier.uri	http://hdl.handle.net/10453/150213
dc.description.abstract	The recent rapid developments in neural networks have stimulated significant performance progress in detecting breakdown in dialogue. The existing research focuses on detecting breakdown from the data label using different features such as word similarity and topic transition; however, these features are insufficient to capture contextual features. Therefore, we used ELMo language model to extract the contextual feature to help to detect the breakdown which faces a challenge due to the basis of human opinion. Another crucial challenge facing breakdown detection is the lack annotated dataset. To take this challenge we used sentiment as the data label with contextualized ELMo embedding model to detect the breakdown in dialogue. The model was evaluated with state-of-the-art models and the results shows the proposed model outperforms other models.
dc.format.extent	64
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Advanced Information Networking and Applications
dc.relation.ispartofseries	Lecture Notes in Networks and Systems
dc.relation.isbasedon	10.1007/978-3-030-75100-5_15
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Sentiment-Driven Breakdown Detection Model Using Contextual Embedding ElMo
dc.type	Chapter
utslib.citation.volume	225
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	recently_added	*
pubs.consider-herdc	false
dc.date.updated	2021-08-24T02:07:56Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	225
dc.location	Switzerland

Abstract:

The recent rapid developments in neural networks have stimulated significant performance progress in detecting breakdown in dialogue. The existing research focuses on detecting breakdown from the data label using different features such as word similarity and topic transition; however, these features are insufficient to capture contextual features. Therefore, we used ELMo language model to extract the contextual feature to help to detect the breakdown which faces a challenge due to the basis of human opinion. Another crucial challenge facing breakdown detection is the lack annotated dataset. To take this challenge we used sentiment as the data label with contextualized ELMo embedding model to detect the breakdown in dialogue. The model was evaluated with state-of-the-art models and the results shows the proposed model outperforms other models.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/150213