Papers by Yonggang Zhang
Improving Chinese Word Segmentation with Wordhood Memory Networks (2020.acl-main)
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| Challenge: | Contextual features are important in Chinese word segmentation (CWS) but it is difficult to integrate wordhood information into existing neural models. |
| Approach: | They propose a neural framework that integrates contextual wordhood information with several popular encoder-decoder combinations for Chinese word segmentation. |
| Outcome: | The proposed framework achieves state-of-the-art performance on five benchmark datasets. |
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations (2020.findings-emnlp)
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| Challenge: | Experimental results show that pre-trained text encoders can perform many NLP tasks with less resource. |
| Approach: | They propose a BERT-based Chinese text encoder enhanced by n-gram representations . they show reasonable performance when ZEN is trained on a small corpus . |
| Outcome: | The proposed encoder incorporates the comprehensive information of both the character sequence and words or phrases it contains. |
Tracing and Dissecting How LLMs Recall Factual Knowledge for Real World Questions (2025.acl-long)
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| Challenge: | Recent advances in large language models have shown promising ability to perform commonsense reasoning. |
| Approach: | They propose a two-dimensional analysis framework that incorporates token back-tracing and token decoding to uncover how LLMs conduct factual knowledge recall. |
| Outcome: | The proposed framework shows that LLMs lack relevant knowledge but struggle to select the most accurate information based on context during the retrieval and rerank phase. |
Generating then Refining for Reliable Knowledge Base Question Answering (2026.acl-long)
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| Challenge: | Existing knowledge base question answering methods generate LFs that are non-executable due to semantic hallucination issue of large language models. |
| Approach: | They propose a "generate-verify-refine" framework for reliable LF generation . they propose ARI-KBQA to generate query paths based on hop-by-hop reasoning . |
| Outcome: | The proposed framework significantly improves model performance with a reduced search space . ARI-KBQA can generate LFs that are non-executable due to semantic hallucination issue . |
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion (2021.acl-long)
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| Challenge: | Existing benchmarks for Knowledge Graph Completion (KGC) are unsatisfactory . |
| Approach: | They propose to use rule-guided train/test generation instead of conventional random split to ensure that each testing sample is predictable with supportive data in the training set. |
| Outcome: | The proposed model improves on existing benchmarks in inferential ability, assumptions, and patterns. |
Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings (2025.acl-long)
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| Challenge: | Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored . |
| Approach: | They propose a PC patching approach to identify modules where input-label mappings function . they observe and verify that key heads utilize input-labeled mappings to generate target labels for new queries. |
| Outcome: | The proposed approach detects modules where input-label mappings function . it also detects that key heads use the mappings to generate labels for new queries . |
Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search (2026.acl-long)
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Tao Feng, Xinke Jiang, Xinyan Hu, Yonggang Zhang, Zhen Tao, Wentao Zhang, Boyang Liu, Wenhao Jiang, Chao Wu
| Challenge: | Existing approaches to augment large language models with external knowledge suffer from a lack of calibration regarding the model’s knowledge boundary. |
| Approach: | They propose a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states. |
| Outcome: | The proposed framework outperforms strong baselines while exhibiting reduced hallucination rates. |
Continual Named Entity Recognition without Catastrophic Forgetting (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (CNER) is a burgeoning area of research . a new paradigm has ushered NER into a non-entity type at the current step t . |
| Approach: | They propose a pooled feature distillation loss that skillfully navigates the trade-off between retaining knowledge of old entity types and acquiring new ones. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on ten CNER settings using three datasets. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge (2020.acl-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging are important fundamental tasks in natural language processing. |
| Approach: | They propose a neural model for Chinese word segmentation and part-of-speech tagging . they incorporate context features and syntactic knowledge for each input character . |
| Outcome: | The proposed model can learn and benefit from existing tools, but its quality may be poor. |