Papers by Xinxin Zhang
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. |
Adversarial Alignment with Anchor Dragging Drift (A3D2): Multimodal Domain Adaptation with Partially Shifted Modalities (2025.acl-long)
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| Challenge: | Domain adaptation is underexplored in multimodal learning environments due to expensive data collection and annotation. |
| Approach: | They propose a bi-alignment scheme to perform drift-drift and anchor-driving matching with partially shifting anchors. |
| Outcome: | The proposed approach achieves superior performance compared with state-of-the-art approaches. |
Amanda: Adaptively Modality-Balanced Domain Adaptation for Multimodal Emotion Recognition (2024.findings-acl)
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| Challenge: | Emotion recognition is a multimodal learning method that can be used for data scarcity. |
| Approach: | They propose to use Adaptively modality-balanced domain adaptation to balance the alignment of different modalities for multimodal emotion recognition. |
| Outcome: | The proposed model outperforms competing models on common datasets on multimodal emotion recognition. |
A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling (2022.emnlp-main)
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| Challenge: | Existing document-level relation extraction methods focus on fully supervised scenarios but in real-world, incomplete labeling is a common problem because the number of entity pairs grows quadratically with the number. |
| Approach: | They propose a positive-unlabeled learning framework for document-level relation extraction (RE) that uses shift and squared ranking loss positive- unlabeles (SSR-PU) learning to solve incomplete labeling problem. |
| Outcome: | The proposed framework outperforms state-of-the-art methods under fully supervised and extremely unlabeled conditions and achieves 14 F1 points over the baseline with incomplete labeling. |
Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models (2025.acl-long)
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| Challenge: | Existing methods for fine-tuning large language models require expensive training on consumer-grade hardwares. |
| Approach: | They propose a sparsity-based approach that introduces trainable sparse adaptations to the weight matrices in the model and offers greater flexibility in selecting fine-tuned parameters. |
| Outcome: | The proposed method outperforms other methods for a simple yet effective baseline for nLP tasks while sacrificing performance. |
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. |
Rank-Aware Negative Training for Semi-Supervised Text Classification (2023.tacl-1)
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| Challenge: | Semi-supervised text classification-based paradigms employ the spirit of self-training, but the accuracy of pseudo-labels can be a problem in real-world scenarios. |
| Approach: | They propose a Rank-aware Negative Training framework to address SSTC in noisy label learning . they rank unlabeled texts based on evidential support from the labeled texts. |
| Outcome: | The proposed framework overcomes state-of-the-art alternatives and achieves competitive performance in other scenarios. |