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More Efficient NLP Model Pre-training with ELECTRA

Posted by Kevin Clark, Student Researcher and Thang Luong, Senior Research Scientist, Google Research, Brain Team Recent advances in language pre-t...

Advancing NLP with Efficient Projection-Based Model Architectures

Posted by Prabhu Kaliamoorthi, Software Engineer, Google Research Deep neural networks have radically transformed natural language processing (NLP)...

Measuring Gendered Correlations in Pre-trained NLP Models

Posted by Kellie Webster, Software Engineer, Google Research Natural language processing (NLP) has seen significant progress over the past several ...

The Language Interpretability Tool (LIT): Interactive Exploration and Analysis of NLP Models

Posted by James Wexler, Software Developer and Ian Tenney, Software Engineer, Google Research As natural language processing (NLP) models become mo...

Two New Datasets for Conversational NLP: TimeDial and Disfl-QA

Posted by Aditya Gupta, Software Engineer and Shyam Upadhyay, Research Scientist, Google Assistant A key challenge in natural language processing (...

Multilingual Sequence-to-Sequence Models for Hebrew NLP

Matan Eyal • Hila Noga • Roee Aharoni • Idan Szpektor • Reut Tsarfaty • (2022)

How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLP

Christian Hardmeier • Marta R. Costa-jussà • Kellie Webster • Will Radford • Su Lin Blodgett • arXiv (2020)

The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models

Ian Tenney • James Wexler • Jasmijn Bastings • Tolga Bolukbasi • Andy Coenen • Sebastian Gehrmann • Ellen Jiang • Mahima Pushkarna • Carey Radebaugh • Emily Reif • Ann Yuan • Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Association for Computational Linguistics (to appear)

Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models

Tianlu Wang • Rohit Sridhar • Diyi Yang • Xuezhi Wang • NAACL 2022 Findings

DoT: An efficient Double Transformer for NLP tasks with tables

Syrine Krichene • Julian Martin Eisenschlos • Thomas Müller • Findings of the Association for Computational Linguistics: ACL 2021

Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Clustering

Deepak Ravichandran • Patrick Pantel • Eduard Hovy • ACL-2005

Measure and Improve Robustness in NLP Models: A Survey

Xuezhi Wang • Haohan Wang • Diyi Yang • NAACL 2022