@inproceedings{singh-etal-2020-identifying,
title = "Identifying Complaints from Product Reviews: A Case Study on {H}indi",
author = "Singh, Raghvendra Pratap and
Haque, Rejwanul and
Hasanuzzaman, Mohammed and
Way, Andy",
editor = "Bhattacharyya, Pushpak and
Sharma, Dipti Misra and
Sangal, Rajeev",
booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2020",
address = "Indian Institute of Technology Patna, Patna, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2020.icon-main.14",
pages = "108--116",
abstract = "Automatic recognition of customer complaints on products or services that they purchase can be crucial for the organisations, 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.",
}
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<abstract>Automatic recognition of customer complaints on products or services that they purchase can be crucial for the organisations, 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.</abstract>
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%0 Conference Proceedings
%T Identifying Complaints from Product Reviews: A Case Study on Hindi
%A Singh, Raghvendra Pratap
%A Haque, Rejwanul
%A Hasanuzzaman, Mohammed
%A Way, Andy
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F singh-etal-2020-identifying
%X Automatic recognition of customer complaints on products or services that they purchase can be crucial for the organisations, 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.
%U https://aclanthology.org/2020.icon-main.14
%P 108-116
Markdown (Informal)
[Identifying Complaints from Product Reviews: A Case Study on Hindi](https://aclanthology.org/2020.icon-main.14) (Singh et al., ICON 2020)
ACL
- Raghvendra Pratap Singh, Rejwanul Haque, Mohammed Hasanuzzaman, and Andy Way. 2020. Identifying Complaints from Product Reviews: A Case Study on Hindi. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 108–116, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).