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Wenxin Hou

7 accepted papers

2024

The Good, The Bad, and Why: Unveiling Emotions in Generative AI

ICML 2024poster

Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions and why. This paper aims to address this gap by incorpor…

Cited by 16SourcePDFScholar
2023

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

ICLR 2023poster

Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization. However, we argue that existing methods might fail to utilize the unlabeled data more effectively since they either use…

2022

Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction

COLING 2022main

Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the sentence. Recently, deep learning approaches have made remarkable progress on this task. Nevertheless, the TOWE task still…

2022

USB: A Unified Semi-supervised Learning Benchmark for Classification

NeurIPS 2022accept

Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation protocols are often constrained to computer vision (CV) tasks. In addition, previous work typically trains deep neural netw…

2021

FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling

NeurIPS 2021poster

The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to cons…

2021

Meta-Adapter: Efficient Cross-Lingual Adaptation With Meta-Learning

ICASSP 2021accepted

Transfer learning from a multilingual model has shown favorable results on low-resource automatic speech recognition (ASR). However, full-model fine-tuning generates a separate model for every target language and is not suitable for deploying and maintaining in production. The key challenge lies in…

Cited by 0SourceScholar
2020

Spoken Language Acquisition Based on Reinforcement Learning and Word Unit Segmentation

ICASSP 2020accepted

The process of spoken-language acquisition has been one of the topics of greatest interest to linguists for decades. By uti-lizing modern machine learning techniques, we simulated this process on computers, which helps to understand it and develop new possibilities of applying this concept on intell…

Cited by 0SourceScholar