Sebastian Gehrmann
Sebastian Gehrmann
Research Scientist, Google
Verified email at - Homepage
Cited by
Cited by
Bottom-up abstractive summarization
S Gehrmann, Y Deng, AM Rush
Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018
LSTMVis: A tool for visual analysis of hidden state dynamics in recurrent neural networks
H Strobelt*, S Gehrmann*, H Pfister, AM Rush
IEEE transactions on visualization and computer graphics 24 (1), 667-676, 2017
Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
S Gehrmann, F Dernoncourt, Y Li, ET Carlson, JT Wu, J Welt, J Foote Jr, ...
PloS one 13 (2), e0192360, 2018
Seq2Seq-Vis: A visual debugging tool for sequence-to-sequence models
H Strobelt*, S Gehrmann*, M Behrisch, A Perer, H Pfister, AM Rush
IEEE transactions on visualization and computer graphics 25 (1), 353-363, 2018
GLTR: Statistical Detection and Visualization of Generated Text
S Gehrmann, H Strobelt, AM Rush
ACL Demo 2019, 2019
ToTTo: A controlled table-to-text generation dataset
AP Parikh, X Wang, S Gehrmann, M Faruqui, B Dhingra, D Yang, D Das
EMNLP 2020, 2020
exBERT: A visual analysis tool to explore learned representations in transformers models
B Hoover, H Strobelt, S Gehrmann
ACL Demo 2020, 2019
End-to-End Content and Plan Selection for Data-to-Text Generation
S Gehrmann, FZ Dai, H Elder, AM Rush
INLG 2018, 2018
Investigating Gender Bias in Language Models Using Causal Mediation Analysis.
J Vig, S Gehrmann, Y Belinkov, S Qian, D Nevo, Y Singer, SM Shieber
NeurIPS, 2020
Behind the scenes: A medical natural language processing project
JT Wu, F Dernoncourt, S Gehrmann, PD Tyler, ET Moseley, ET Carlson, ...
International journal of medical informatics 112, 68-73, 2018
LSTM Networks Can Perform Dynamic Counting
M Suzgun, S Gehrmann, Y Belinkov, SM Shieber
Deep Learning and Formal Languages Workshop at ACL 19, 2019
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
I Tenney*, J Wexler*, J Bastings, T Bolukbasi, A Coenen, S Gehrmann, ...
EMNLP Demo 2020, 2020
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
S Gehrmann*, H Strobelt*, R Krüger, H Pfister, AM Rush
VAST 2019, 2019
Encoder-agnostic adaptation for conditional language generation
ZM Ziegler*, L Melas-Kyriazi*, S Gehrmann, AM Rush
arXiv preprint arXiv:1908.06938, 2019
The GEM💎 benchmark: Natural language generation, its evaluation and metrics
S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ...
arXiv preprint arXiv:2102.01672, 2021
Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations
P Das, T Sercu, K Wadhawan, I Padhi, S Gehrmann, F Cipcigan, ...
Nature Biomedical Engineering 5 (6), 613-623, 2021
Interpretability and analysis in neural NLP
Y Belinkov, S Gehrmann, E Pavlick
Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020
E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language
H Elder, S Gehrmann, A O’Connor, Q Liu
INLG 2018, 0
Memory-augmented recurrent neural networks can learn generalized Dyck languages
M Suzgun, S Gehrmann, Y Belinkov, SM Shieber
arXiv preprint arXiv:1911.03329, 2019
Deploying AI methods to support collaborative writing: a preliminary investigation
S Gehrmann*, L Urke*, O Amir, BJ Grosz
Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human …, 2015
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