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Found 8 papers, 4 papers with code
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues
Further, it employs a fulfillment representation layer for learning how many task attributes have been fulfilled in the dialogue, an importance predictor component for calculating the importance of task attributes.
Rethinking Semi-supervised Learning with Language Models
Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks.
Just Mix Once: Worst-group Generalization by Group Interpolation
Advances in deep learning theory have revealed how average generalization relies on superficial patterns in data.
PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels
In this paper, we propose PARS: Pseudo-Label Aware Robust Sample Selection, a hybrid approach that combines the best from all three worlds in a joint-training framework to achieve robustness to noisy labels.
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations
Predominantly, two formulations are used for sentence-pair tasks: bi-encoders and cross-encoders.
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Improving the expressiveness of neural vocoding with non-affine Normalizing Flows
In the waveform reconstruction task, the proposed model closes the naturalness and signal quality gap from the original PW to recordings by $10\%$, and from other state-of-the-art neural vocoding systems by more than $60\%$.
Universal Neural Vocoding with Parallel WaveNet
We present a universal neural vocoder based on Parallel WaveNet, with an additional conditioning network called Audio Encoder.
The Weighted Kendall and High-order Kernels for Permutations
We propose new positive definite kernels for permutations.
