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Found 5 papers, 3 papers with code
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models
Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, then using the feedback to update the model.
A Versatile Diffusion-based Generative Refiner for Speech Enhancement
As the refiner, we train a diffusion-based generative model by utilizing a dataset consisting of clean speech only.
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE).
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER
We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.
Nested Named Entity Recognition via Second-best Sequence Learning and Decoding
When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive.
Ranked #2 on
Nested Mention Recognition
on ACE 2005
