Actividades

Computational linguistics is a relatively mature field which has previously been of interest largely to students with a background in either quantitative linguistics or computer science. But the rapid rise of generative artificial intelligence has created a tremendous interest in computational linguistics (i) from more traditional linguists, (ii) from researchers in entirely different fields, and (iii) from the general public. This interest creates a new challenge of how to teach students from such varied backgrounds about CL and NLP. In this talk I present some lessons learned from my own experiences in teaching computational linguistics to non-computational students, including experience designing a MOOC about NLP on edX.

The talk will cover our experience in teaching various techniques and methodologies of Digital Humanities to students of (especially English) philology. The scope of methodologies ranges from digitization and encoding (through various projects like An Anglo-Saxon Dictionary Online https://bosworthtoller.com/, the Database of MEdieval CZech textual sources in translation https://mecz.kreas.ff.cuni.cz/ or the Thesauri of Czech https://najdislovo.cz/ as well as summer schools), data-mining or web-scraping, data wrangling and transformation (in Data Processing course), diachronic language corpus construction, transformation, querying and analysis (Introduction to English Diachronic Corpora) to statistical analysis, visualisation, machine learning and AI powered classification (in Digital Methodologies and AI in Social Sciences and Humanities, as well as in Research Methods in Applied Linguistics). This year, the Faculty of Arts, Charles University has also founded a Center for Digital Humanities (https://cdh.ff.cuni.cz/) whose goal is to foster and coordinate not only DH research, but also teaching of digital methodologies to students of social sciences and humanities. The talk will also cover our initial experiences in this effort and our plans for the future.

Over the last few years, Large Language Models (LLMs) not only have made a leap forward in the Natural Language Processing (NLP) but have also impacted other areas of Artificial Intelligence such as knowledge representation or automatic reasoning (for tasks such as problem solving, planning, learning). Such advances in the linguistic capacity thanks to LLMS have made a huge difference in how intelligent these machines are.
In this dynamic panorama and in this article/presentation, we would like to open a multilateral discussion with the different stakeholders (academia, industry, public administration, civil society) to share the different experiences concerning the relevant role of the “computational linguist” in the AI era highlighting three issues: 1) How to shape the future of the “computational linguists” in this era? 2) What are the skills he/she should have in order to meet the growing needs of the NLP and the AI field? 3) What are the best practices and experiences in building such competences?
In this open discussion, we will share our experience at UCM on how we envisage the pivotal role of the computational linguist within the growing demand for language specialists and professionals. In this respect, we will assess the keys for a successful development of competences through training and teaching new generations of professionals, capable of understanding, developing, evaluating and improving these models across the different NLP tasks. In this sense, over generations, the Complutense University of Madrid has decided to take a stance in favor of building capacities and competences of computational linguists with a wide and proven experience since 1996.
This presentation aims to contribute in gaining insights on “Teaching NPL in the era of artificial intelligence” by raising the key questions and challenges in training computational linguists and preparing them in order to successfully integrate into the professional and research field of NLP.

The higher education (HE) sector benefits every nation’s economy and society at large. However, their contributions are being challenged by advanced technologies like generative AI tools. We provide a comprehensive demonstration of the use of generative AI tools towards assessment design, and pedagogic practice, and evidence of their impact on learning. We provided a detailed review of the use of generative AI tools in academia and experimented using three assessment instruments from the data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that generative AI tools possess subject knowledge, problem-solving, analytical, critical thinking, and presentation skills, and thus, can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines reveals the limitation of the generative AI tools. Based on our findings, we make recommendations on how AI tools can be utilised for teaching and learning in HE.

Artificial Intelligence is everywhere and transformative. The power of NLP, conversational AI and generative AI has taken the world by storm from a research, commercial and public perspective. In December 2022, a 100 million users used OpenAI's ChatGPT on a weekly basis. In 2023, it went mainstream in the business world. In 2024, this disruption is and aspects of AI, has the potential to reshape globally businesses, customer engagement and other sectors and thus a game changer. For the future employable NLP/AI/computational linguistics - multidisciplinary practitioners - both teaching, learning, research and project work are fundamental. To address some of this – I will talk about ‘reflective experiences of teaching both NLP and research informed multimodal AI solutions. I will cover: (1) ‘where are we with NLP and generative AI in 2024? (2) what technologies underpin the teaching of this and how does it improve employability? (3) what is nature of the multimodal AI research discussed in the talk? A key take away from the session (4) what strategies are used to enable research informed teaching based on the example discussed?