ABSTRACT. Competitiveness has been a subject of study in the manufacturing and related sectors since the early 1990s. However, only recently have some researchers started to examine the tourism and hospitality competitiveness, both conceptually and empirically, with a particular focus on tourism destinations and the hotel industry. The goal of this article is to review the published studies on destination and hotel competitiveness, provide critiques, and point out future directions in tourism and hotel competitiveness research. Such a review shall provide researchers with a good understanding of the current status of competitiveness research and with a vision for advancing the existing knowledge of destination and hotel competitiveness.

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We propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a single CPU. Starting from a public multilingual BERT checkpoint, our final model is 6x smaller and 27x faster, and has higher accuracy than a state-of-theart multilingual baseline. We show that our model especially outperforms on low-resource languages, and works on codemixed input text without being explicitly trained on codemixed examples. We showcase the effectiveness of our method by reporting on part-of-speech tagging and morphological prediction on 70 treebanks and 48 languages.

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