Search Results for author: Yunlong Jiao

Found 8 papers, 4 papers with code

Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues

1 code implementation26 May 2023 Yue Feng, Yunlong Jiao, Animesh Prasad, Nikolaos Aletras, Emine Yilmaz, Gabriella Kazai

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.

Attribute Language Modelling +1

Rethinking Semi-supervised Learning with Language Models

2 code implementations22 May 2023 Zhengxiang Shi, Francesco Tonolini, Nikolaos Aletras, Emine Yilmaz, Gabriella Kazai, Yunlong Jiao

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.

Pseudo Label Semi-Supervised Text Classification +1

Just Mix Once: Worst-group Generalization by Group Interpolation

no code implementations21 Oct 2022 Giorgio Giannone, Serhii Havrylov, Jordan Massiah, Emine Yilmaz, Yunlong Jiao

Advances in deep learning theory have revealed how average generalization relies on superficial patterns in data.

Learning Theory

PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels

no code implementations26 Jan 2022 Arushi Goel, Yunlong Jiao, Jordan Massiah

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.

Learning with noisy labels Pseudo Label

Improving the expressiveness of neural vocoding with non-affine Normalizing Flows

no code implementations16 Jun 2021 Adam Gabryś, Yunlong Jiao, Viacheslav Klimkov, Daniel Korzekwa, Roberto Barra-Chicote

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

no code implementations1 Feb 2021 Yunlong Jiao, Adam Gabrys, Georgi Tinchev, Bartosz Putrycz, Daniel Korzekwa, Viacheslav Klimkov

We present a universal neural vocoder based on Parallel WaveNet, with an additional conditioning network called Audio Encoder.

Speech Synthesis

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