Papers by Yongcheng Zhang

6 papers
Dynamic Parallel Tree Search for Efficient LLM Reasoning (2025.acl-long)

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Challenge: Recent methods focus on search accuracy while overlooking computational efficiency.
Approach: They propose a parallelism framework that dynamically optimizes reasoning path in inference.
Outcome: The proposed framework improves efficiency by 2-4 on average while maintaining or even surpassing existing reasoning algorithms in accuracy.
Refine, Align, and Aggregate: Multi-view Linguistic Features Enhancement for Aspect Sentiment Triplet Extraction (2024.findings-acl)

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Challenge: Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiment and opinion terms.
Approach: They propose to use multi-view linguistic features enhancement to explore the prior indication effect in the “Refine, Align, and Aggregate” learning process to enhance aspect-opinion relations.
Outcome: The proposed model achieves state-of-the-art on several benchmark datasets and is robust to state- of-the art constraints.
Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction (2025.coling-main)

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Challenge: Existing table-filling methods decompose the ASQP task into subtasks without considering the association between sentiment elements.
Approach: They propose a simple yet effective Unified Grid Tagging Scheme to extract sentiment quadruplets in one shot . they leverage syntactic dependency tree and AMR graph to enrich association between sentiment elements .
Outcome: The proposed model extracts all sentiment elements in quads for a given review to explain the reason for the sentiment.
Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention (2024.lrec-main)

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Challenge: Existing methods based on deep learning struggle to grasp idiom semantics due to the figurative meanings of many idiomas deviating from their literal interpretations.
Approach: They propose a Chinese idiom cloze test to capture comprehensive idiomatics and a semantic sense contrastive learning module to enhance the representation of idiomics.
Outcome: The proposed model outperforms state-of-the-art models on the Chinese idiom cloze test and on other benchmark datasets.
Gardener: An Agentic AI System for Single-Cell RNA Sequence Analysis (2026.acl-demo)

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Challenge: Existing large language models encode workflow progress as conversational state and rely on cloud-centric execution, which hinders traceability and auditability.
Approach: Gardener is an open-source desktop application for macOS and windows under the Apache License 2.0.
Outcome: Gardener is released as an open-source desktop application for macOS and Windows under the Apache License 2.0.
RTCFake: Speech Deepfake Detection in Real-Time Communication (2026.findings-acl)

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Challenge: Existing detection studies focus on offline simulations and struggle to cope with complex distortions introduced during RTC transmission.
Approach: They propose a large-scale speech deepfake dataset tailored for RTC scenarios . the dataset is constructed by transmitting speech through multiple social media and conferencing platforms .
Outcome: The proposed dataset is constructed by transmitting speech through multiple mainstream social media and conferencing platforms, enabling precise pairing between offline and online speech.

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