Jasmijn Bastings

Hello

Hi, welcome to my website! My name is Jasmijn (she/her), and I’m a Research Scientist at Google. Currently I’m interested in the following topics within Natural Language Processing (NLP) / Computational Linguistics (CL):

• Interpretability and analysis of NLP models
• Machine Learning for NLP
• Efficiency / Efficient models

I received my PhD from ILLC, University of Amsterdam, where I was advised by Wilker Aziz, Ivan Titov and Khalil Sima’an.

News

• I started a YouTube channel for BlackboxNLP. Check it out and subscibe here: youtube.com/@blackboxnlp.
• You can now find me on Mastodon: @jasmijn@sigmoid.social or https://sigmoid.social/@jasmijn.

Publications

Recent publications

• “Will You Find These Shortcuts?” A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification. Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, Katja Filippova. EMNLP 2022. [blog]
• Training Text-to-Text Transformers with Privacy Guarantees. Natalia Ponomareva, Jasmijn Bastings, Sergei Vassilvitskii. Findings of ACL 2022.
• The MultiBERTs: BERT Reproductions for Robustness Analysis. Thibault Sellam, Steve Yadlowsky, Jason Wei, Naomi Saphra, Alexander D’Amour, Tal Linzen, Jasmijn Bastings, Iulia Turc, Jacob Eisenstein, Dipanjan Das, Ian Tenney, Ellie Pavlick. ICLR 2022.
• Diagnosing ai explanation methods with folk concepts of behavior. Alon Jacovi, Jasmijn Bastings, Sebastian Gehrmann, Yoav Goldberg, Katja Filippova. 2021.
• Simple Recurrence Improves Masked Language Models. Tao Lei, Ran Tian, Jasmijn Bastings, Ankur P Parikh.

Highlighted publications

• The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?. Jasmijn Bastings, Katja Filippova. BlackboxNLP 2020.
• Interpretable neural predictions with differentiable binary variables. Jasmijn Bastings, Wilker Aziz, Ivan Titov. ACL 2019.
• Joey NMT: A Minimalist NMT Toolkit for Novices. Julia Kreutzer, Jasmijn Bastings, Stefan Riezler. EMNLP 2019. [code]
• Graph convolutional encoders for syntax-aware neural machine translation Jasmijn Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an. EMNLP 2017.

You can find a full list of my publications on my Google Scholar profile.

Blog posts

• The Annotated Encoder-Decoder. Explains implementing RNN-based NMT models in PyTorch.

Code

• Interpretable Neural Predictions with Differentiable Binary Variables contains the HardKuma distribution that allows (hybrid) binary samples (with true zeros and ones) that allow gradients to pass through.
• Joey NMT is an easy-to-use, educational, and benchmarked NMT toolkit for novices that I developed with Julia Kreutzer and is currently maintained by Mayumi Ohta.
• FREVAL is an all-fragments parser evaluation metric that I developed with Khalil Sima’an.

Talks

• EMNLP 2020 Blackbox NLP. The Elephant in the Interpretability Room. (PDF)
• ACL 2019. Interpretable Neural Predictions with Differentiable Binary Variables (Google Slides)
• EMNLP 2017. Graph Convolutional Encoders for Syntax-Aware Neural Machine Translation (Google Slides)

CV

• 2019-Now, Research Scientist, Google. Berlin & Amsterdam.
• 2015-2020. PhD in AI, ILLC, University of Amsterdam. Defended 8 October 2020.*
• 2009-2012, MSc in AI, University of Amsterdam.
• 2006-2009, BSc in AI, Utrecht University.

Reviewing / Area Chair / Committees

I was a co-organizer of:

• Blackbox NLP 2022 (co-located with EMNLP 2022)
• Blackbox NLP 2021 (co-located with EMNLP 2021)

I was area chair (AC) / action editor (AE) for the following conferences:

• ACL (2021, 2022, 2023) (Interpretability and Analysis of Models for NLP)
• EMNLP (2021, 2022) (Interpretability and Analysis of Models for NLP)
• ACL rolling review (2021-2022)
• EACL (2021) (Machine Learning for NLP)
• NAACL (2021) (Interpretability and Analysis of Models for NLP)

I reviewed for the following conferences and workshops:

• ACL (2019, 2020)
• EMNLP (2018, 2019, 2020)
• CoNNL (2018, 2019)
• ICLR (2020)
• MT Summit (2019)
• WMT (2018, 2019)
• Analyzing and interpreting neural networks for NLP (BlackboxNLP, 2019, 2020)
• Debugging Machine Learning Models (Debug ML, ICLR Workshop, 2019)
• Workshop on Neural Generation and Translation (WNGT, 2018, 2019, 2020)
• Workshop on Representation Learning for NLP (RepL4NLP, 2020)
• Workshop on Structured Prediction for NLP (SPNLP, 2019)

Contact

• Mastodon
• You can find me on Twitter: @BastingsJasmijn.
• My code is on Github: github.com/bastings.

https://orcid.org/0000-0002-5445-4417