Benchmarking Automated Review Response Generation for the Hospitality Domain (2020)

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.

## introduction online customer reviews play a significant role in e-commerce and specifically in the tourism and hospitality industry. websites such as tripadvisor, booking.com and yelp offer customers the opportunity to share their experiences in the form of reviews relating to restaurants, cafés, hotels and other business types. to date, tripadvisor boasts more than 860 million customer reviews and opinions worldwide 1 . while these reviews serve as an important point of reference for potential customers who value and rely on electronic word-of-mouth recommendations @xcite , it has been shown that they also offer businesses an opportunity to influence the public discourse and foster positive customer relations by responding to these reviews appropriately (see @xcite ; @xcite ). however, given the sheer amount of online reviews, composing these responses is a time-consuming and costly endeavour for any business. in this paper, we report on automated response generation for hospitality reviews, specifically restaurants and hotels, in both english and german. while a similar task has previously been tackled for product reviews in online stores in chinese @xcite and for smartphone app reviews in english @xcite , the application domain of hospitality reviews is, to the best of our knowledge, novel. in addition, our investigations are applied to two languages, which allows for a valuable idea of how well the approach generalises across languages. the experiments conducted in this paper leverage the approach proposed by @xcite , who extend the basic attentional sequence-to-sequence (seq2seq) neural network @xcite framework for conditioned text generation. we find that applying the proposed solu-tion to hospitality review-response pairs yields a considerable decrease in overall performance compared to the results reported in the original paper in the context of app reviews. an empirical investigation into possible causes for the discrepancy provides evidence to suggest that the task is considerably more challenging in our target domain. thus, we establish a preliminary baseline for future work on automatic response generation for hospitality reviews. ## seq2seq review response generation following previous work (e.g., @xcite al. (2019); zhao et al. (2019) ), we tackle the task of review response generation using the popular seq2seq encoder-decoder framework originally proposed by @xcite in the context of neural machine translation (nmt) and successfully applied in a broad range of nlp tasks, including abstractive text summarisation @xcite and conversational dialogue systems @xcite , among others. the central idea behind the seq2seq framework is as follows: given an input (source) sequence of length n, @xmath0, . . . , x n }, and a corresponding output (target) sequence of length m, @xmath1, . . . , y m }, we aim to learn a probabilistic mapping function g(•) that allows us to predict a probable target sequence ŷ for a novel source sequence. ## conclusion and future work we have applied a system designed for app review response generation to the domain of hospitality reviews in both english and german. according to a range of automatic evaluation metrics, the results of our experiments indicate that the benefits provided by the extended seq2seq model proposed by @xcite for their application domain do not transfer to our target domain. a subsequent empirical investigation into the different datasets has revealed that the nature of review-response pairs in the domain of smartphone app reviews differs significantly to that of the hospitality domain. given our results, we have established a preliminary baseline for automated review response generation in the domain of english and german hospitality reviews that uses a classic attentional seq2seq architecture. in future work, we intend to continue investigating potential improvements for this task, incorporating improved aspect-level sentiment detection and knowledge grounding techniques, as well as conducting more detailed evaluations with the help of human judges.