TourBERT: A pretrained language model for the tourism industry
This work addresses domain-specific NLP challenges for the tourism industry, but it is incremental as it applies an existing method to new data.
The authors tackled the problem of improving natural language processing for tourism-specific tasks by pretraining BERT on a domain-specific corpus, resulting in TourBERT outperforming BERT in all evaluated tourism tasks.
The Bidirectional Encoder Representations from Transformers (BERT) is currently one of the most important and state-of-the-art models for natural language. However, it has also been shown that for domain-specific tasks it is helpful to pretrain BERT on a domain-specific corpus. In this paper, we present TourBERT, a pretrained language model for tourism. We describe how TourBERT was developed and evaluated. The evaluations show that TourBERT is outperforming BERT in all tourism-specific tasks.