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The Impact of Trans...
The Impact of Translating Resource-Rich Datasets to Low-Resource Languages Through Multi-Lingual Text Processing
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- Ghafoor, Abdul (författare)
- Sukkur IBA University, Pakistan
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- Imran, Ali Shariq (författare)
- Norwegian University of Science and Technology (NTNU), Norway
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- Daudpota, Sher Muhammad (författare)
- Sukkur IBA University, Pakistan
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- Kastrati, Zenun, 1984- (författare)
- Linnéuniversitetet,Institutionen för informatik (IK)
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- Soomro, Abdullah (författare)
- Sukkur IBA University, Pakistan
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- Batra, Rakhi (författare)
- Sukkur IBA University, Pakistan
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- Wani, Mudasir Ahmad (författare)
- Norwegian University of Science and Technology, Norway
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(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 124478-124490
- Relaterad länk:
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https://doi.org/10.1...
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https://lnu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Urdu is still considered a low-resource language despite being ranked as world’s 10th most spoken language with nearly 230 million speakers. The scarcity of benchmark datasets in low-resource languages has led researchers to utilize more ingenious techniques to curb the issue. One such option widely adopted is to use language translation services to replicate existing datasets from resource-rich languages such as English to low-resource languages, such as Urdu. For most natural language processing tasks, including polarity assessment, words translated via Google translator from one language to another often change the meaning. It results in a polarity shift causing the system’s performance degradation, particularly for sentiment classification and emotion detection tasks. This study evaluates the effect of translation on the sentiment classification task from a resource-rich language to a low-resource language. It identifies and enlists words causing polarity shift into five distinct categories. It further finds the correlation between the language with similar roots. Our study shows 2-3 percentage points performance degradation in sentiment classification due to polarity shift as a result of translation from resource-rich languages to low-resource languages.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
Nyckelord
- Multilingual text processing
- sentiment classification
- polarity assessment
- low resource language
- language translation
- BiLSTM
- Conv1D
- English
- Urdu
- German
- Hindi.
- Informatik
- Information Systems
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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