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Found 4 papers, 1 papers with code
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings
Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces.
Context Analysis for Pre-trained Masked Language Models
Pre-trained language models that learn contextualized word representations from a large un-annotated corpus have become a standard component for many state-of-the-art NLP systems.
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models
Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset.
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots
In this work, we propose a context-aware self-attentive NLU (CASA-NLU) model that uses multiple signals, such as previous intents, slots, dialog acts and utterances over a variable context window, in addition to the current user utterance.
