Natural Language Processing (NLP) concerns the development of techniques that enable computers to fruitfully process, manipulate and understand language as used by humans. NLP has seen a drastic increase in research throughout the last decade, boosted by disruptive advancements in deep learning, more specifically with the development of word embedding representations, attention models, transformer architectures and complex neural language models. These advancements have seen widespread adoption due to the fact that they enable taking advantage of huge amounts of unlabeled data in an unsupervised fashion. At the same time, recent models have shown impressive state-of-the-art results across different NLP tasks.
Within LIACC, NLP research covers topics such as language models, benchmarking, question generation/answering, paraphrase generation, argument mining, machine translation for low-resourced languages, and historical language processing. LIACC also conducts cross-research between NLP and deep learning, including cross-lingual approaches, adversarial training, unsupervised language adaptation, and transfer learning. Research on NLP is at the core of relevant research projects with the public administration and is sparking several collaborations with other institutions.