# About

I am a postdoctoral researcher at the Institute for Logic, Language, and Computation at the University of Amsterdam. My interests lie at the intersection of natural language processing and probabilistic modelling. My research is part of the European UTTER project.

### Current Interests

- Probabilistic Modelling
- Natural Language Generation
- Minimum Bayes Risk

### Selected Publications

**An Approximate Sampler for Energy-based Models with Divergence Diagnostics**

Bryan Eikema, Germán Kruszewski, Cristopher R Dance, Hady Elsahar, Marc Dymetman in

*Transactions on Machine Learning Research*, 2022

**Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation**

Bryan Eikema and Wilker Aziz in

*Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*, 2022

**Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation**

Bryan Eikema and Wilker Aziz in

*Proceedings of the 28th International Conference on Computational Linguistics (COLING)*, 2020

*Best Paper Award*

### Talks

- Cambridge NLIP Seminar Series 2022: Decoding is deciding under uncertainty
- AI Seminar Series KU 2022: A Distribution-Aware Decision Rule for NMT
- Unbabel, ILLC CLS 2021: The Inadequacy of the Mode in NMT
- COLING 2020: Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation

### Projects

mbr-nmt: Sampling-based Minimum Bayes Risk decoding in Python.AEVNMT.pt: A PyTorch-based framework for deep generative models of text.