Papers by Huiyao Chen

4 papers
Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering (2026.acl-long)

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Challenge: Existing long document question answering systems process texts as flat sequences or use heuristic chunking, which overlooks the discourse structures that guide human comprehension.
Approach: They propose a discourse-aware hierarchical framework that leverages rhetorical structure theory for long document question answering.
Outcome: The proposed framework exhibits strong robustness across diverse document types and linguistic settings.
Semantic Role Labeling from Chinese Speech via End-to-End Learning (2024.findings-acl)

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Challenge: Semantic role labeling (SRL) has traditionally focused on text input.
Approach: They propose an end-to-end approach for SRL from speech integrating ASR and SRL in a joint-learning framework, focusing on the Chinese language.
Outcome: The proposed model improves on the Chinese Proposition Bank 1.0 dataset and the existing model with improved performance.
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)

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Challenge: Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed .
Approach: They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities.
Outcome: The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech .
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models (2025.findings-acl)

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Challenge: Semantic role labeling (SRL) is a crucial task of natural language processing (NLP).
Approach: They propose to equip LLMs with retrieval-augmented generation and self-correction mechanisms to enable SRL to perform better in Chinese and English.
Outcome: The proposed method achieves state-of-the-art in Chinese and English on three widely-used benchmarks.

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