In this work we examine the task of generating more specific responses for online reviews in the hospitality domain by identifying generic responses in the training data, filtering them and fine-tuning the generation model.
A formal comparison shows that the system trained on the normalised transcriptions achieves better results in word error rate (WER) (29. 39%) but underperforms at the character level, suggesting dialectal transcriptions offer a viable solution for downstream applications where dialectal differences are important.
The task of document-level text simplification is very similar to summarization with the additional difficulty of reducing complexity.
Online customer reviews are of growing importance for many businesses in the hospitality industry, particularly restaurants and hotels.
Geotagging historic and cultural texts provides valuable access to heritage data, enabling location-based searching and new geographically related discoveries.