Two PhD Positions in Conversational AI (# of pos: 2)

Come and join us to work on algorithmic solutions to challenges in conversational intelligence! We are a diverse team of researchers based at multiple universities in the Netherlands, with expertise in information retrieval, natural language processing, machine learning, and customer psychology. As part of the LESSEN project , which is funded by the Dutch Research Council (NWO), we will work together with industrial stakeholders to develop chat-based conversational agents for lesser resourced tasks, domains, and scenarios. Scientific challenges of the project are (i) the development of neural architectures that are compute and resource efficient; (ii) domain adaptation and data augmentation; and (iii) safety, privacy, and transparency.

The Information Retrieval Lab Amsterdam (IRLab) at the Informatics Institute of the University of Amsterdam invites application for two fully-funded, four-year PhD positions on Conversational AI. There are two PhD projects corresponding to this vacancy: [A] the first is aimed at making neural networks for conversational agents more efficient, and [B] the second is aimed at generating synthetic data for training conversational agents.

PhD position A
The PhD project corresponding to this vacancy is aimed at making neural networks for conversational agents more efficient. How can we reduce their data and compute footprint, both at training time and at inference time? Knowledge distillation has emerged as a potential solution, allowing small student models to learn from, and emulate the performance of, large teacher models. In this project you will develop neural architecture search methods to effectively emulate large conversational models subject to limited memory and parameter budgets. Research for this opening will be conducted within the IRLab under supervision of Prof. Maarten de Rijke.

PhD position B
The PhD project corresponding to this vacancy aims to generate synthetic data for training conversational agents. One of the limitations of developing models for conversational agents is the availability and cost of labelled training data. A common approach to alleviate this need is to increase the amount of training data by data augmentation. In this project we will use controllable text generation techniques to develop alternative and unbiased dialogue trajectories to train conversational systems on. Research for this opening will be conducted within the IRLab under supervision of Prof. Evangelos Kanoulas.

What are you going to do?

Tasks and responsibilities:

• Conducting research in Conversational AI, resulting in academic publications in peer-reviewed international conferences/journals, datasets, and open-source code;
• Assisting in teaching courses in the bachelor and master programmes and supervising Bachelor and Master theses.