# Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain arXiv: https://arxiv.org/abs/2311.14727 ยท HTML: https://mlsanity.com/paper/2311.14727/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 63 references that are in the map. - First posted on arXiv 20 Nov 2023. ## Builds on (most cited first, 30 of 30) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022) - [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015) - [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020) - [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018) - [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021) - [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020) - [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019) - [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022) - [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://mlsanity.com/paper/2310.03668/) (2023) - [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021) - [Translation Artifacts in Cross-lingual Transfer Learning](https://mlsanity.com/paper/2004.04721/) (2020) - [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021) - [Revisiting Machine Translation for Cross-lingual Classification](https://mlsanity.com/paper/2305.14240/) (2023) - [GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain](https://mlsanity.com/paper/2109.02555/) (2021) - [InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER](https://mlsanity.com/paper/2203.03903/) (2022) - [SemEval-2013 Task 2: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.06806/) (2019) - [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://mlsanity.com/paper/2210.12623/) (2022) - [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019) - [SemEval-2015 Task 10: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02387/) (2019) - [T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks](https://mlsanity.com/paper/2212.10548/) (2022) - [Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation](https://mlsanity.com/paper/2010.03486/) (2020) - [Social Analysis of Young Basque Speaking Communities in Twitter](https://mlsanity.com/paper/2109.03487/) (2021) - [Cross-lingual Argument Mining in the Medical Domain](https://mlsanity.com/paper/2301.10527/) (2023) - [On the Role of Morphological Information for Contextual Lemmatization](https://mlsanity.com/paper/2302.00407/) (2023) ## Built on by (most cited first, 1 of 1) - [Automate Knowledge Concept Tagging on Math Questions with LLMs](https://mlsanity.com/paper/2403.17281/) (2024) ## Abstract (arXiv) The rising influence of social media platforms in various domains, including tourism, has highlighted the growing need for efficient and automated Natural Language Processing (NLP) strategies to take advantage of this valuable resource. However, the transformation of multilingual, unstructured, and informal texts into structured knowledge still poses significant challenges, most notably the never-ending requirement for manually annotated data to train deep learning classifiers. In this work, we study different NLP techniques to establish the best ones to obtain competitive performances while keeping the need for training annotated data to a minimum. To do so, we built the first publicly available multilingual dataset (French, English, and Spanish) for the tourism domain, composed of tourism-related tweets. The dataset includes multilayered, manually revised annotations for Named Entity Recognition (NER) for Locations and Fine-grained Thematic Concepts Extraction mapped to the Thesaurus of Tourism and Leisure Activities of the World Tourism Organization, as well as for Sentiment Analysis at the tweet level. Extensive experimentation comparing various few-shot and fine-tuning techniques with modern language models demonstrate that modern few-shot techniques allow us to obtain competitive results for all three tasks with very little annotation data: 5 tweets per label (15 in total) for Sentiment Analysis, 30 tweets for Named Entity Recognition of Locations and 1K tweets annotated with fine-grained thematic concepts, a highly fine-grained sequence labeling task based on an inventory of 315 classes. We believe that our results, grounded in a novel dataset, pave the way for applying NLP to new domain-specific applications, reducing the need for manual annotations and circumventing the complexities of rule-based, ad-hoc solutions.