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Comparing Test Sets with Item Response Theory
Recent years have seen numerous NLP datasets introduced to evaluate the ...
06/01/2021 ∙ by Clara Vania, et al. ∙
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English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too
Intermediate-task training has been shown to substantially improve pretr...
05/26/2020 ∙ by Jason Phang, et al. ∙
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Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?
While pretrained models such as BERT have shown large gains across natur...
05/01/2020 ∙ by Yada Pruksachatkun, et al. ∙
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jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
We introduce jiant, an open source toolkit for conducting multitask and ...
03/04/2020 ∙ by Yada Pruksachatkun, et al. ∙
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Do Attention Heads in BERT Track Syntactic Dependencies?
We investigate the extent to which individual attention heads in pretrai...
11/27/2019 ∙ by Phu Mon Htut, et al. ∙
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Generalized Inner Loop Meta-Learning
Many (but not all) approaches self-qualifying as "meta-learning" in deep...
10/03/2019 ∙ by Edward Grefenstette, et al. ∙
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Inducing Constituency Trees through Neural Machine Translation
Latent tree learning(LTL) methods learn to parse sentences using only in...
09/22/2019 ∙ by Phu Mon Htut, et al. ∙
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Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs
Though state-of-the-art sentence representation models can perform tasks...
09/05/2019 ∙ by Alex Warstadt, et al. ∙
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The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction
In recent years, sequence-to-sequence models have been very effective fo...
07/21/2019 ∙ by Phu Mon Htut, et al. ∙
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Grammar Induction with Neural Language Models: An Unusual Replication
A substantial thread of recent work on latent tree learning has attempte...
08/29/2018 ∙ by Phu Mon Htut, et al. ∙
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Training a Ranking Function for Open-Domain Question Answering
In recent years, there have been amazing advances in deep learning metho...
04/12/2018 ∙ by Phu Mon Htut, et al. ∙
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