Papers by Zhihao Lin
ATLAS: Improving Lay Summarisation with Attribute-based Control (2024.acl-short)
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| Challenge: | Lay summarisation aims to produce scientific summaries that are comprehensible to non-experts. |
| Approach: | They propose an abstractive summarisation approach that can control properties contributing to overall "layness" they evaluate ATLAS on a combination of biomedical lay summarization datasets. |
| Outcome: | The proposed approach outperforms state-of-the-art summarisation metrics on biomedical datasets and shows that it can be discriminatory and emergently influenced. |
Exploring the Capability of Multimodal LLMs with Yonkoma Manga: The YManga Dataset and Its Challenging Tasks (2024.findings-emnlp)
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| Challenge: | YManga dataset is the first specifically designed for yonkoma manga understanding . |
| Approach: | They propose to use a dataset of 1,015 yonkoma strips with 10,150 human annotations to define three tasks for panel sequence detection, intent generation and description generation for masked panels. |
| Outcome: | The proposed dataset contains 1,015 high-quality yonkoma strips with 10,150 human annotations. |
OD-RTE: A One-Stage Object Detection Framework for Relational Triple Extraction (2023.acl-long)
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| Challenge: | Existing pipelines for relational triple extraction are underutilizing regional information of triple. |
| Approach: | They propose a one-stage Object Detection framework for Relational Triple Extraction . framework uses vertices-based bounding box detection and global relational triple region detection . |
| Outcome: | The proposed framework could extract all types of triples on two widely used datasets. |
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement (2020.coling-main)
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| Challenge: | Existing methods for information extraction are based on pipelining to extract entities from unstructured judgment documents . a large number of judgment documents are released on China Judgments Online . |
| Approach: | They propose a legal triplet extraction system for drug-related criminal judgment documents . they annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain . |
| Outcome: | The proposed system extracts entities and semantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework. |
Evaluating Readability and Faithfulness of Concept-based Explanations (2024.emnlp-main)
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| Challenge: | Existing methods for evaluating concepts from different perspectives lack a unified formalization. |
| Approach: | They propose a formal definition of concepts generalizing to diverse concept-based explanations’ settings and apply it to other types of explanations or tasks. |
| Outcome: | Extensive experimental analysis was carried out to determine the evaluation measures for explanation evaluation measures. |
Prototype Tuning: A Meta-Learning Approach for Few-Shot Document-Level Relation Extraction with Large Language Models (2025.findings-naacl)
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| Challenge: | Few-Shot Document-Level Relation Extraction (FSDLRE) aims to develop models capable of generalizing to new categories with minimal support examples. |
| Approach: | They propose a meta-training approach to train Large Language Models to improve their ICL capabilities . they construct simulated episodes using relation types that do not overlap with test corpus . |
| Outcome: | Experimental results show that the proposed approach outperforms baseline models on few-shot tasks. |
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature (2022.emnlp-main)
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| Challenge: | Existing datasets for lay summarisation are limited in size and scope, hindering the development of data-driven approaches. |
| Approach: | They propose to use two new datasets for the lay summarisation of biomedical research articles to characterise their lay summaries. |
| Outcome: | The proposed datasets are compared with existing datasets and show they can be leveraged to support different audiences and applications. |
EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention (2022.findings-emnlp)
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| Challenge: | Existing methods for story generation still suffer from problems of relevance and coherence. |
| Approach: | They propose a novel neural generation model which maps contextual and event features to event sequences with a cross-attention mechanism and exploits logical relatedness between events. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic and human evaluations and shows that it can leverage contextual and event features. |
Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)
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| Challenge: | Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks. |
| Approach: | They propose a task to break the boundaries between different modal NER tasks by using a unified data format for inputs from different modalités. |
| Outcome: | The proposed task breaks the boundaries between different modal NER tasks and is a unified implementation of them. |
“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning (2024.lrec-main)
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| Challenge: | Existing methods for generating humorous puns are limited and require a broad spectrum of commonsense and worldly skills. |
| Approach: | They propose a GAN-based approach that employs semantic pruning and contrastive learning to generate humorous puns using a model that captures the semantic nuances of puns. |
| Outcome: | The proposed model produces semantically coherent and humorous puns while ensuring both correctness and humor. |
Enhancing Biomedical Lay Summarisation with External Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Existing approaches to lay summarisation are reliant on the source article, which is unlikely to include all the information necessary for a lay audience. |
| Approach: | They augment existing biomedical lay summarisation dataset with article-specific knowledge graphs that contain detailed information on relevant biomedically related concepts. |
| Outcome: | The proposed methods improve readability and explanation of technical concepts by integrating graph-based domain knowledge within lay summarisation models. |
Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)
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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang
| Challenge: | Existing approaches to reduce label noise rely on heuristics and sample losses. |
| Approach: | They propose a method that transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. |
| Outcome: | Empirically, the proposed approach improves over strong baselines on a wide range of tasks including text classification and speech recognition. |
Stable Language Guidance for Vision–Language–Action Models (2026.acl-long)
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| Challenge: | Existing vision-Language-Action models are notoriously brittle to linguistic perturbations. |
| Approach: | They propose a probabilistic framework that disentangles physical affordance from semantic execution. |
| Outcome: | The proposed framework disentangles physical affordance from semantic execution. |
Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach (2026.acl-industry)
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| Challenge: | Literary translation requires balancing expression fluency with literary effect due to the scarcity of high-quality training data and the difficulty of capturing nuanced quality trade-offs. |
| Approach: | They propose a multi-aspect iterative refinement framework that generates high-quality translation references and preference data through specialized LLM translators. |
| Outcome: | The proposed models outperform the ground truth for SFT by 8.65 CEA100 points while leveraging an explicit reward model for GRPO yields an additional 1.51 point improvement. |
Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing studies focus on internal features of Chinese named entity recognition, but neglect other lingual modalities. |
| Approach: | They propose a bilingual enhancement module for Chinese Named Entity Recognition . they integrate rich English information into Chinese representation and use it to learn the interaction between bilinguals and dependent information within Chinese. |
| Outcome: | The proposed model can learn the interaction of bilinguals and dependent information within Chinese. |
NGEP: A Graph-based Event Planning Framework for Story Generation (2022.aacl-short)
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| Challenge: | Current approaches to story generation are based on end-to-end neural generation models, such as BART, to generate event sequences. |
| Approach: | They propose a novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) event planning approaches on multiple criteria and compares with existing models on the downstream task of story generation. |
PAM: Enhancing General Alignment of Large Reasoning Models through Priority-Aware Metacognition (2026.acl-long)
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| Challenge: | Existing studies indicate that System-2 thinking alone does not transfer to the general alignment domain. |
| Approach: | They propose to use priority-aware metacognition to help LRMs understand human preferences and monitor and regulate their thinking process. |
| Outcome: | The proposed model improves general alignment performance by 10 points on helpfulness and harmless benchmarks. |
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)
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Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang
| Challenge: | Homographic puns have a long history in human writing, widely used in written and spoken literature, which intended as jokes. |
| Approach: | They propose a WordNet-encoded model to settle polysemy of homographic puns and a word weighted model for recognizing them. |
| Outcome: | The proposed model can distinguish between homographic pun and non-homographic pun texts. |