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Benchmarking Automated Review Response Generation for the Hospitality Domain

Kew, Tannon; Amsler, Michael; Ebling, Sarah (2020). Benchmarking Automated Review Response Generation for the Hospitality Domain. In: Workshop on Natural Language Processing in E-Commerce, Barcelona, Spain, 12 December 2020. Association for Computational Linguistics, 43-52.

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.

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:12 December 2020
Deposited On:11 Jan 2021 09:11
Last Modified:21 Apr 2022 16:42
Publisher:Association for Computational Linguistics
OA Status:Green
Free access at:Related URL. An embargo period may apply.
Official URL:https://www.aclweb.org/anthology/2020.ecomnlp-1.5
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