Grzegorz Chrupała

News

Twitter | Mastodon

Recent

• I will attend EMNLP 2022 in Abu Dhabi, and present Learning English with Peppa Pig.
• I am a Senior Area Chair for Speech and Multimodality for ACL 2023.
• I am a Senior Area Chair for Language Grounding and Multi-Modality for EACL 2023.
• New PhD candidate Gaofei Shen will work on analysis and control techniques for spoken language applications
• Learning English with Peppa Pig has been published in TACL

People

• Gaofei Shen PhD candidate in the InDeep project.
• Gabriele Sarti. PhD candidate in the InDeep project.
• Hosein Mohebbi. PhD candidate in the InDeep project.
• Chris Emmery. PhD candidate, NLP for cybersecurity.

Alumni

• Bertrand Higy. Postdoc, Understanding visually grounded spoken language via multi-tasking.
• Patrick Bos. E-science engineer, Understanding visually grounded spoken language via multi-tasking.
• Christiaan Meijer. E-science engineer, Understanding visually grounded spoken language via multi-tasking.
• Ákos Kádár. PhD thesis: Learning Visually Grounded and Multilingual Representations

Publications

For the complete list of publications check: Google Scholar | Semantic Scholar | DBLP | ACL Anthology | ORCID

Selected papers

1. Nikolaus, M., Alishahi, A. & Chrupała, G. (2022). Learning English with Peppa Pig. Transactions of the Association for Computational Linguistics, 10, 922–936.
   Paper | Code
2. Chrupała, G. (2022). Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniques. Journal of Artificial Intelligence Research, 73, 673-707.
   Paper
3. Chrupała, G., & Alishahi, A. (2019). Correlating Neural and Symbolic Representations of Language. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 2952-2962).
   Paper | Code
4. Chrupała, G., Gelderloos, L., & Alishahi, A. (2017). Representations of language in a model of visually grounded speech signal. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 613-622).
   Paper | Code
5. Kádár, A., Chrupała, G., & Alishahi, A. (2017). Representation of linguistic form and function in recurrent neural networks. Computational Linguistics, 43(4):761-780.
   Paper | Code

Recent Talks

• Learning language from Peppa Pig. ILLC seminar, University of Amsterdam.
• Visually Grounded Models of Spoken Language and their Analysis. Lecture at ALPS Winter School. Video
• Investigating neural representations of speech and language. Keynote at the Nordic Conference on Computational Linguistics (NoDaLiDa), October 2019. Slides

Supervision

Selected MSc theses

• Aayushi Pandey. 2020. Emotion recognition in a model of visually grounded speech. Tilburg University.
• Dmitrijs Surenans. 2020. Machine Learning Explainability In Finance: An Application to Default Risk Analysis. Tilburg University.
• Dennis de Groot. 2019. Finding Structure in Neural Network Activation Patterns via Representational Similarity and Convolutional Kernels. Tilburg University.
• Mark van der Laan. 2018. Encoding of speaker identity in a Neural Network model of Visually Grounded Speech perception. Tilburg University.
• Lieke Gelderloos. 2016. Tilburg University. Levels of representation in a recurrent neural model of visually grounded language learning. Tilburg University. See also Coling 2016 paper: From phonemes to images: levels of representation in a recurrent neural model of visually grounded language learning.
• Ákos Kádár. 2014. Grounded learning for source code component retrieval. Tilburg University
• Antoaneta Baltadzhieva. 2014. Predicting question quality in question answering forums. Tilburg University
• Huijing Deng. 2013. Probabilistic Models of API Retrieval. Saarland University. (See also Deng and Chrupała. 2014. Semantic approaches to software component retrieval with English queries. LREC.)

Bio

Grzegorz Chrupała is an Associate Professor at the Department of Cognitive Science and Artificial Intelligence at Tilburg University. Previously he did postdoctoral research at the Spoken Language Systems group at Saarland University. He received his doctoral degree from the School of Computing at Dublin City University. His research focuses on computational models of language learning from multimodal signals such as speech and vision and on the analysis and interpretation of representations emerging in deep learning architectures. He regularly serves as Senior Area Chair for major NLP and AI conferences such as ACL and EMNLP. He was one of the creators of the popular BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP. His research has been funded by the Dutch Research Council (NWO), via ASDI and NWA-ORC grants.

Contact

Twitter: @gchrupala
Mastodon: @gchrupala@sigmoid.social
Web: grzegorz.chrupala.me
Email: grzegorz@chrupala.me