Julian Martin Eisenschlos
Julian Martin Eisenschlos
NLP Researcher, Google
Verified email at - Homepage
Cited by
Cited by
TAPAS: Weakly Supervised Table Parsing via Pre-training
J Herzig, PK Nowak, T Müller, F Piccinno, JM Eisenschlos
Proceedings of ACL 2020, 2020
Multifit: Efficient multi-lingual language model fine-tuning
JM Eisenschlos, S Ruder, P Czapla, M Kardas, S Gugger, J Howard
Proceedings of EMNLP 2020, 2019
Understanding tables with intermediate pre-training
JM Eisenschlos, S Krichene, T Müller
Findings of EMNLP 2020, 2020
Time-aware language models as temporal knowledge bases
B Dhingra, JR Cole, JM Eisenschlos, D Gillick, J Eisenstein, WW Cohen
Transactions of the Association for Computational Linguistics 10, 257-273, 2022
Open Domain Question Answering over Tables via Dense Retrieval
J Herzig, T Müller, S Krichene, JM Eisenschlos
Proceedings of NAACL 2021, 2021
SoftSort: A Continuous Relaxation for the argsort Operator
S Prillo, JM Eisenschlos
Proceedings of ICML 2020, 2020
MATE: Multi-view Attention for Table Transformer Efficiency
JM Eisenschlos, M Gor, T Müller, WW Cohen
arXiv preprint arXiv:2109.04312, 2021
Fool Me Twice: Entailment from Wikipedia Gamification
JM Eisenschlos, B Dhingra, J Bulian, B Börschinger, J Boyd-Graber
Proceedings of NAACL 2021, 2021
TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training
T Müller, JM Eisenschlos, S Krichene
SemEval 2021, 2021
DoT: An efficient Double Transformer for NLP tasks with tables
S Krichene, T Müller, JM Eisenschlos
Findings of ACL 2021, 2021
Evaluating likely accuracy of metadata received from social networking system users based on user characteristics
VI Nandagopal, CA Andrews, O Rouhani-Kalleh, JM Eisenschlos
US Patent 10,061,797, 2018
Modifying text according to a specified attribute
TL Julian Martin Eisenschlos
US Patent App. 16/699,825, 2021
3-Calabi-Yau Algebras from Steiner Systems
JM Eisenschlos, M Suárez-Álvarez
Master thesis, Universidad de Buenos Aires, 2013
Adapting Language Models to Temporal Knowledge
B Dhingra, D Gillick, J Eisenstein, J Cole, JM Eisenschlos, WW Cohen
Predicting the Quality of New Contributors to a Crowdsourcing System
JM Eisenschlos, VN Iyer
NeurIPS: Crowdsourcing and Machine Learning Workshop, 2014
Framework for Recasting Table-to-Text Generation Data for Tabular Inference
A Jena, V Gupta, M Shrivastava, JM Eisenschlos
Table-To-Text generation and pre-training with TABT5
E Andrejczuk, JM Eisenschlos, F Piccinno, S Krichene, Y Altun
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Articles 1–17