Papers by Chenye Zhao
RMSSinger: Realistic-Music-Score based Singing Voice Synthesis (2023.findings-acl)
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| Challenge: | Existing methods for singing voice synthesis are limited to fine-grained music scores . manual adjustment destroys regularity of note durations, making fine-grain music scores "crushed" |
| Approach: | They propose a method to synthesize singing voices given realistic music scores . they use real-music-score-based Singing Voice Synthesis to generate high-quality voices . |
| Outcome: | The proposed method eliminates manual annotation and simplifies phoneme-level mel-note alignment. |
FastDiff 2: Revisiting and Incorporating GANs and Diffusion Models in High-Fidelity Speech Synthesis (2023.findings-acl)
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| Challenge: | Experimental results show that Generative adversarial networks sacrifice sample diversity for quality and speed, while diffusion models exhibit outperformed sample quality and diversity at a high computational cost. |
| Approach: | They propose to combine GANs and diffusion probabilistic models to achieve better sample quality and diversity. |
| Outcome: | The proposed models outperform GANs and diffusion models in speech synthesis . the proposed models enjoy an efficient 4-step sampling process and exhibit better sample diversity . |
Towards Identifying Fine-Grained Depression Symptoms from Memes (2023.acl-long)
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| Challenge: | Mental health disorders are a major economic burden for society and are projected to rise to a staggering US $6 trillion by 2030. |
| Approach: | They propose to use memes to identify fine-grained depression symptoms from memes . they benchmark RESTORE on 20 strong monomodal and multimodal methods . |
| Outcome: | The proposed method can predict fine-grained depression symptoms better than existing models that overlook implicit connections between visual and textual elements of a meme. |
Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation (2021.emnlp-main)
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| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose a method that applies instance-specific temperature scaling to the teacher and student predictions. |
| Outcome: | The proposed method outperforms the state-of-the-art on all datasets and on multiple datasets. |
ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation (2024.findings-acl)
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| Challenge: | Until recently, zero-shot stance detection was limited to in-domain tasks. |
| Approach: | They propose a method for stance detection that trains a model that can generalize well to unseen targets across multiple domains. |
| Outcome: | The proposed method generalizes well to unseen targets across multiple domains over baselines on most benchmarks. |
C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection (2023.acl-long)
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| Challenge: | Recent advances in zero-shot stance detection are limited to English and Chinese . stance can provide useful information for important events such as policymaking and presidential elections. |
| Approach: | They present a Chinese dataset for zero-shot stance detection that is the first for ZSSD. |
| Outcome: | The proposed dataset is the first Chinese dataset for zero-shot stance detection. |
EZ-STANCE: A Large Dataset for Zero-Shot Stance Detection (2023.findings-emnlp)
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| Challenge: | EZ-STANCE is a large dataset for zero-shot stance detection in english . it includes both noun-phrase targets and claim targets covering a wide range of domains. |
| Approach: | They present a large English ZSSD dataset with 30,606 annotated text-target pairs . they propose to transform EZ-STANCE into the NLI task by applying two simple yet effective prompts to noun-phrase targets. |
| Outcome: | The proposed dataset includes noun-phrase targets and claim targets covering a wide range of domains. |
Bilingual Zero-Shot Stance Detection (2025.acl-long)
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| Challenge: | a study focuses on noun-phrase and claim targets within bilingual ZSSD scenarios . a dataset focusing on claim targets with a low occurrence of shared words is also explored . |
| Approach: | They use a bilingual bilingual ZSSD dataset to investigate the use of zero-shot stance detection. |
| Outcome: | The proposed dataset is the first to examine this difficult setting in bilingual ZSSD . it focuses on noun-phrase and claim targets within in-domain and out-of-domain bilingual scenarios . |
StanceAttack: Adversarial Attack for Stance Detection (2026.findings-acl)
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| Challenge: | pretrained language models (PLMs) have greatly enhanced stance detection, but they remain vulnerable to adversarial attacks. |
| Approach: | They propose an adversarial attack method that uses ChatGPT to create adversarials that can mislead well-trained stance detection models. |
| Outcome: | The proposed method outperforms existing adversarial methods with higher success rates and fewer retries on two benchmark datasets. |