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Identifying complaints from product reviews in low-resource scenarios via neural machine translation

Singh, Raghvendra Pratap, Haque, Rejwanul orcid logoORCID: 0000-0003-1680-0099, Hasanuzzaman, Mohammed orcid logoORCID: 0000-0003-1838-0091 and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2020) Identifying complaints from product reviews in low-resource scenarios via neural machine translation. In: ICON 2020: 17th International Conference on Natural Language Processing, 18-21 Dec 2020, IIT Patna, India (Online).

Abstract
Automatic recognition of customer complaints on products or services that they purchase can be crucial for the organizations, multinationals and online retailers since they can exploit this information to fulfil their customers’ expectations including managing and resolving the complaints. Recently, researchers have applied supervised learning strategies to automatically identify users’ complaints expressed in English on Twitter. The downside of these approaches is that they require labeled training data for learning, which is expensive to create. This poses a barrier for them being applied to low-resource languages and domains for which task-specific data is not available. Machine translation (MT) can be used as an alternative to the tools that require such task-specific data. In this work, we use state-of-the-art neural MT (NMT) models for translating Hindi reviews into English and investigate performance of the downstream classification task (complaints identification) on their English translations.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Additional Information:This paper is from the practicum of M.Sc. in Computing, Dublin City University
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
Computer Science > Machine learning
Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Publisher:Association for Computational Linguistics (ACL)
Copyright Information:© 2020 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) Research Centres Programme (Grant No. 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:25291
Deposited On:04 Jan 2021 13:15 by Raghvendra Pratap Singh . Last Modified 11 May 2023 14:25
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