This project brings together two trends in NLP. First, with large language models achieving increasingly impressive results on standard benchmarks, the NLP community pays increasing attention to evaluations that go beyond standard benchmark sets. One line of research has returned to challenge sets (e.g. King and Falkedal 1990, Lehmann et al. 1996, Ribeiro et al. 2020). Another point of attention is this year's special theme track of ACL, reality check, raising questions of what happens when models are used in the real world.

Second, as a fast-developing machine learning technique, reinforcement learning (RL) targets sequential decision-making tasks and is capable of adaptively interacting with the users to learn a model with target behavior (Sutton and Barto 2018). Since it is particularly powerful by taking the feedback from users into account and updating the strategy adaptively according to actual needs, in recent years, RL has been widely applied and shown as a well-suited solution in various NLP tasks (Uc-Cetina et al. 2022).

In this project, we aim to start such a research line that will bring these two research directions together and will explore possibilities of using RL to target specific behavior of models. This could be training specifically to avoid a social undesirable bias, but also to exhibit robust behavior on a specific phenomenon (e.g. negation, specific syntactic structures, temporal information interacting with truth values).

Your duties

• Conduct research within the scope of the project culminating in a successful dissertation
• Writing academic articles and presenting your work on conferences
• Taking part in Hybrid Intelligence project activities

• Master degree in Computational Linguistics or a related field, such as Artificial Intelligence, Linguistics or Computer Science, with focus on NLP
• Good machine learning skills
• Good technical skills (e.g. sufficient programming to setup and run NLP experiments)
• The ideal candidate also has experience with the following:
  • Reinforcement Learning
  • Linguistic Analysis

Fixed-term contract: 1 year.

• 4-year full-time position in a collaborative, social, and dedicated team
  
  
• Embedding in the Computational Linguistics and Text Mining Lab at Vrije Universiteit Amsterdam as well as the Intelligent Systems Lab at Utrecht University
  
  
• Joint supervision by an expert in methodology in NLP (Antske Fokkens) and in Reinforcement learning in NLP (Shihan Wang).
  
  
• PhD students are regular employees, so full participation in the department, decent salary, pension and health contributions, etc.
  
  

The salary will be in accordance with university regulations for academic personnel and amounts €2,541 (PhD) per month during the first year and increases to €3,247 (PhD) per month during the fourth year, based on a full-time employment. The job profile: is based on the university job ranking system and is vacant for at least 0.8 FTE.

The appointment will initially be for 1 year. After a satisfactory evaluation of the initial appointment, the contract will be extended for a duration of 4 years.
Additionally, Vrije Universiteit Amsterdam offers excellent fringe benefits and various schemes and regulations to promote a good work/life balance, such as:

• a maximum of 41 days of annual leave based on full-time employment
• 8% holiday allowance and 8.3% end-of-year bonus
• solid pension scheme (ABP)
• discount on (and occasionally exclusive access to) theater performances and courses at the Griffioen Cultural Center
• a wide range of sports facilities which staff may use at a modest charge

Vrije Universiteit Amsterdam

The ambition of Vrije Universiteit Amsterdam is clear: to contribute to a better world through outstanding education and ground-breaking research. We strive to be a university where personal development and commitment to society play a leading role. A university where people from different disciplines and backgrounds collaborate to achieve innovations and to generate new knowledge. Our teaching and research encompass the entire spectrum of academic endeavor - from the humanities, the social sciences and the natural sciences through to the life sciences and the medical sciences.

Vrije Universiteit Amsterdam is home to more than 30,000 students. We employ over 5,500 individuals. The VU campus is easily accessible and located in the heart of Amsterdam's Zuidas district, a truly inspiring environment for teaching and research.

Diversity
We are an inclusive university community. Diversity is one of our most important values. We believe that engaging in international activities and welcoming students and staff from a wide variety of backgrounds enhances the quality of our education and research. We are always looking for people who can enrich our world with their own unique perspectives and experiences.

Faculty of Humanities
The Faculty of Humanities links a number of fields of study: Language, Literature and Communication, Art & Culture, History, Antiquities and Philosophy. Our teaching and research focus on current societal and scientific themes: from artificial intelligence to visual culture, from urbanization to the history of slavery, from 'fake news' in journalism to communication in organizations. We strive to ensure small group sizes. Innovative education and interdisciplinary research are our hallmarks.

Working at the Faculty of Humanities means making a real contribution to the quality of leading education and research in an inspiring and personal work and study climate. We employ more than 350 staff members, and we are home to around 2,000 students.

About the Computational Linguistics and Text Mining Lab (CLTL) & Intelligence Systems Group

CLTL is one of the leading groups in the area of multilingual language understanding, language modelling and resources, interdisciplinary research and computational linguistic methodology. Check out our website www.cltl.nl for more details about our research and teaching.

The intelligent systems group in Utrecht studies intelligent systems in both fundamental and application-oriented ways. One main research focus of the group is reinforcement learning and its usage in intelligent agents, covering both single-agent and multi-agent learning.