[Paper Review] TourBERT: A pretrained language model for the tourism industry

Veronika Arefieva, Roman Egger|arXiv (Cornell University)|Jan 19, 2022

Natural Language Processing Techniques4 citations

TL;DR

TourBERT is a domain-adapted BERT model pre-trained on tourism-specific text data to improve performance on travel-related natural language tasks. Fine-tuned on tourism datasets, it outperforms standard BERT across all evaluated tasks, demonstrating the value of domain-specific pretraining for the tourism industry.

ABSTRACT

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.

Motivation & Objective

• To develop a specialized language model tailored for the tourism domain, where general-purpose models like BERT underperform due to domain distribution gaps.
• To investigate whether pretraining on tourism-specific corpora improves performance on downstream tourism NLP tasks compared to general-domain BERT.
• To create a publicly available, high-performing language model for applications such as travel recommendation, sentiment analysis, and intent classification in tourism.
• To validate the effectiveness of domain-adapted pretraining through systematic evaluation on benchmark tourism datasets.

Proposed method

• Pretraining a BERT-based architecture on a large-scale, curated corpus of tourism-related text, including travel reviews, hotel descriptions, and destination guides.
• Using masked language modeling (MLM) and next sentence prediction (NSP) objectives during pretraining, similar to standard BERT, but on domain-specific data.
• Fine-tuning the pre-trained TourBERT model on multiple tourism-specific downstream tasks such as aspect-based sentiment analysis and intent classification.
• Employing transfer learning to adapt the model to specific tourism NLP tasks with limited labeled data.
• Evaluating performance using standard NLP metrics like F1-score, accuracy, and macro-F1 on benchmark tourism datasets.
• Comparing TourBERT’s performance directly against standard BERT and RoBERTa on identical downstream tasks to isolate the impact of domain-specific pretraining.

Experimental results

Research questions

• RQ1 Does pretraining BERT on tourism-specific text lead to improved performance on downstream tourism NLP tasks compared to general-domain BERT?
• RQ2 How does TourBERT compare to standard BERT and RoBERTa in terms of zero-shot and fine-tuned performance on tourism-specific benchmarks?
• RQ3 To what extent does domain-specific pretraining reduce the performance gap between general-purpose models and specialized tourism applications?
• RQ4 What is the impact of domain-specific vocabulary and sentence structures on the learned representations in TourBERT?
• RQ5 Can TourBERT generalize across diverse tourism NLP tasks such as sentiment analysis, intent detection, and entity recognition?

Key findings

• TourBERT outperforms standard BERT across all evaluated tourism-specific tasks, demonstrating consistent gains in F1-score and accuracy.
• The model achieves state-of-the-art performance on benchmark datasets for aspect-based sentiment analysis and intent classification in tourism.
• Fine-tuning TourBERT on downstream tasks leads to significant improvements over fine-tuned BERT, particularly in low-resource settings.
• The domain-adapted pretraining process results in more semantically coherent and contextually relevant representations for tourism-related text.
• TourBERT shows robustness across diverse tourism text types, including user reviews, travel guides, and hotel descriptions.
• The improvements are attributed to the model’s exposure to domain-specific terminology, syntactic patterns, and semantic structures during pretraining.