
Resumo: Despite the recent and impressive advances in the Natural Language Processing and Machine Learning areas, most models are still trained on general / large-scale data, assuming an "one-size-fits-all" approach. However, for most real-world applications, personalised / adaptative models are more suitable and can bring significant improvements as well as reduce unwanted biases. In this talk I will present mine and my team's work on NLP models for applications that require personalisation / adaptation. I will present our methods and results for classification-based (e.g. disinformation classification) and generation-based (e.g. machine translation) tasks, discussing the challenges of researching personalised NLP.