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Found 5 papers, 2 papers with code
Controlling the Imprint of Passivization and Negation in Contextualized Representations
Contextualized word representations encode rich information about syntax and semantics, alongside specificities of each context of use.
A Closer Look at Parameter Contributions When Training Neural Language and Translation Models
We analyze the learning dynamics of neural language and translation models using Loss Change Allocation (LCA), an indicator that enables a fine-grained analysis of parameter updates when optimizing for the loss function.
Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging
This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks.
On the differences between BERT and MT encoder spaces and how to address them in translation tasks
Various studies show that pretrained language models such as BERT cannot straightforwardly replace encoders in neural machine translation despite their enormous success in other tasks.
Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
In this paper we introduce a new natural language processing dataset and benchmark for predicting prosodic prominence from written text.
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