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Found 3 papers, 2 papers with code
Generalization Measures for Zero-Shot Cross-Lingual Transfer
A model's capacity to generalize its knowledge to interpret unseen inputs with different characteristics is crucial to build robust and reliable machine learning systems.
End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model Pretraining
In light of recent successes in large scale audio pretraining, we revisit the performance comparison between two-stage and end-to-end model and find that audio based language models pretrained using weak self-supervised objectives match or exceed the performance of similarly trained two-stage models, and further, that the choice of pretraining objective substantially effects a model's ability to be adapted to the disfluency removal task.
Learning high-dimensional causal effect
We experiment on this dataset using Dragonnet architecture (Shi et al. (2019)) and modified architectures.
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