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    <abstract>Online customer reviews are of growing importance for many businesses in the hospitality industry, particularly restaurants and hotels. Managerial responses to such reviews provide businesses with the opportunity to influence the public discourse and to attain improved ratings over time. However, responding to each and every review is a time-consuming endeavour. Therefore, we investigate automatic generation of review responses in the hospitality domain for two languages, English and German. We apply an existing system, originally proposed for review response generation for smartphone apps. This approach employs an extended neural network sequence-to-sequence architecture and performs well in the original domain. However, as shown through our experiments, when applied to a new domain, such as hospitality, performance drops considerably. Therefore, we analyse potential causes for the differences in performance and provide evidence to suggest that review response generation in the hospitality domain is a more challenging task and thus requires further study and additional domain adaptation techniques.</abstract>
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%0 Conference Proceedings
%T Benchmarking Automated Review Response Generation for the Hospitality Domain
%A Kew, Tannon
%A Amsler, Michael
%A Ebling, Sarah
%Y Zhao, Huasha
%Y Sondhi, Parikshit
%Y Bach, Nguyen
%Y Hewavitharana, Sanjika
%Y He, Yifan
%Y Si, Luo
%Y Ji, Heng
%S Proceedings of Workshop on Natural Language Processing in E-Commerce
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain
%F kew-etal-2020-benchmarking
%X Online customer reviews are of growing importance for many businesses in the hospitality industry, particularly restaurants and hotels. Managerial responses to such reviews provide businesses with the opportunity to influence the public discourse and to attain improved ratings over time. However, responding to each and every review is a time-consuming endeavour. Therefore, we investigate automatic generation of review responses in the hospitality domain for two languages, English and German. We apply an existing system, originally proposed for review response generation for smartphone apps. This approach employs an extended neural network sequence-to-sequence architecture and performs well in the original domain. However, as shown through our experiments, when applied to a new domain, such as hospitality, performance drops considerably. Therefore, we analyse potential causes for the differences in performance and provide evidence to suggest that review response generation in the hospitality domain is a more challenging task and thus requires further study and additional domain adaptation techniques.
%U https://aclanthology.org/2020.ecomnlp-1.5
%P 43-52

Markdown (Informal)

[Benchmarking Automated Review Response Generation for the Hospitality Domain](https://aclanthology.org/2020.ecomnlp-1.5) (Kew et al., EcomNLP 2020)

• Benchmarking Automated Review Response Generation for the Hospitality Domain (Kew et al., EcomNLP 2020)

ACL

• Tannon Kew, Michael Amsler, and Sarah Ebling. 2020. Benchmarking Automated Review Response Generation for the Hospitality Domain. In Proceedings of Workshop on Natural Language Processing in E-Commerce, pages 43–52, Barcelona, Spain. Association for Computational Linguistics.