Papers by Guodong Shen
Negative Focus Detection via Contextual Attention Mechanism (D19-1)
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| Challenge: | Negation is a universal but complicated linguistic phenomenon that reverses the polarity of a statement or its property into opposite. |
| Approach: | They propose a framework which consists of a Bidirectional Long Short-Term Memory neural network and a Conditional Random Fields layer to capture contextual information. |
| Outcome: | The proposed framework improves on the SEM’12 shared task corpus, yielding an absolute improvement of 2.11% over the state-of-the-art. |
Pre-training Cross-Modal Retrieval by Expansive Lexicon-Patch Alignment (2024.lrec-main)
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| Challenge: | Recent large-scale vision-language pre-training relies on image-text global alignment by contrastive learning and is further boosted by fine-grained alignment in a weakly contrastive manner for cross-modal retrieval. |
| Approach: | They propose expansive lexicon-patch alignment (ELA) to align image patches with a vocabulary rather than only the words explicitly in the text for annotation-free alignment and information augmentation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on cross-modal retrieval and can learn representative fine-grained information. |
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following (2025.findings-acl)
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| Challenge: | Existing role-playing datasets mostly contribute to controlling role style and knowledge boundaries, but overlook role-following in instruction-follower scenarios. |
| Approach: | They propose a fine-grained role-playing and instruction-following composite benchmark, named RoleMRC, which includes multi-turn dialogues between ideal roles and humans, including free chats or discussions upon given passages . |
| Outcome: | The proposed model improves instruction-following without compromising general role-playing and reasoning capabilities. |
Synergistic Interplay between Search and Large Language Models for Information Retrieval (2024.acl-long)
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| Challenge: | Information retrieval (IR) is an indispensable technique for locating relevant resources from vast amounts of data. |
| Approach: | They propose a framework that facilitates information refinement through synergy between RMs and LLMs. |
| Outcome: | The proposed framework improves the performance of large-scale retrieval benchmarks on web searches and low-resource retrieval tasks. |
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification (2022.acl-long)
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| Challenge: | Existing work on event-centric reasoning fails to model event-level correlations . Existing studies limit their scope to specific scenarios or overlook event- level correlations. |
| Approach: | They propose to pre-train a general Correlation-aware context-to-Event Transformer for event-centric reasoning by highlighting event-level correlations with effective training. |
| Outcome: | The proposed model is applicable to a wide range of event-centric reasoning scenarios, considering its versatility of event correlation types, application formulations, and reasoning types. |
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base (D19-1)
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| Challenge: | Recent approaches to handle large knowledge base decompose tasks into subtasks and solve them sequentially. |
| Approach: | They propose a multi-task learning framework that resolves coreference in conversations . they propose enabling shared supervisions and type-aware entity detection model . |
| Outcome: | The proposed framework improves overall F1 score from 67% to 79% on a large-scale conversational question answering dataset. |
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning (2020.emnlp-main)
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| Challenge: | Existing methods for integrating knowledge graphs into pre-trained language models have been poorly implemented. |
| Approach: | They propose a self-supervised entity masking scheme that exploits relational knowledge underlying the text. |
| Outcome: | The proposed model achieves improved performance on five benchmarks, including question answering and knowledge base completion. |
Re-Reading Improves Reasoning in Large Language Models (2024.emnlp-main)
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| Challenge: | Unlike thought-eliciting prompting methods, RE2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. |
| Approach: | They introduce a simple, yet general and effective prompting method, RE2, which rereads the question as input. |
| Outcome: | The proposed method demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT. |
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision (2022.findings-naacl)
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| Challenge: | Distant supervision uses triple facts to label corpus for relation extraction, leading to wrong labeling and long-tail problems. |
| Approach: | They propose a model to enrich distantly-supervised sentences with entity types by injecting context-free and -related backgrounds into sentences to alleviate sentence-level wrong labeling. |
| Outcome: | The proposed model achieves state-of-the-art on benchmarks and in overall and long-tail performance. |
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention (2020.coling-main)
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| Challenge: | Existing approaches to handle wrong labeling and long-tail relations are labor-intensive and scarce training data. |
| Approach: | They propose a neural network to handle wrong labeling and long-tail relations by collaborating relation-augmented attention. |
| Outcome: | The proposed neural network improves the state-of-the-art on the NYT dataset . |
Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together (N19-1)
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| Challenge: | Neural networks equipped with self-attention have parallelizable computation and the ability to capture both long-range and local dependencies. |
| Approach: | They propose a novel attention mechanism called "Multi-mask Tensorized Self-Attention" it captures pairwise and global dependencies by a compatibility function composed of dot-product and additive attentions . |
| Outcome: | The proposed model outperforms CNN-/RNN-/attention-based models on nine NLP benchmarks with compelling memory- and time-efficiency. |
Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever (2024.findings-acl)
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Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, Daxin Jiang
| Challenge: | Large-scale retrieval is indispensable in information-seeking tasks such as open-domain question answering and knowledgegrounded dialogue. |
| Approach: | They propose to use a large language model (LLM) to augment a query with its potential answers by prompting LLMs with a composition of the query and the query’s in-domain candidates. |
| Outcome: | The proposed method breaks brute-force combinations of retrievers with LLMs and lifts the performance of zero-shot retrieval to be very competitive on benchmark datasets. |
New Compendium of a Myriad of Plants: A New Dataset Describing Ancient Chinese Plants (2026.findings-acl)
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| Challenge: | Existing approaches to digitize ancient Chinese texts and extract information from them are shallow and inconsistent with modern realities. |
| Approach: | They propose to expand ancient Chinese datasets using large language model . they focus on Great Compendium of Myriad Flowers, an ancient plants dataset . |
| Outcome: | The proposed model can extract plant-related information from classical Chinese poetry and prose. |
Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information (2024.findings-emnlp)
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| Challenge: | Existing methods focus on local optimal while ignoring sole-mention disambiguation boosted by richer context from other mentions’ disambiguating processes. |
| Approach: | They propose an approach to extracting medical entity disambiguation using memory mechanism and memorized entity information (M3E) they use a memory mechanism module that performs memory caching, retrieval, fusion and cross-network residual to aid the disambiguations of remaining mentions. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two benchmark datasets. |
Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)
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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou
| Challenge: | Existing methods to classify QA text contain rich sentiment information. |
| Approach: | They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines. |
| Outcome: | The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit. |
Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction (2021.findings-emnlp)
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| Challenge: | Aspect-level sentiment classification (ALSC) is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect. |
| Approach: | They propose a span-based anti-bias aspect representation learning framework that eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects’ prior sentiment. |
| Outcome: | The proposed framework achieves state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction. |
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification (2024.eacl-long)
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| Challenge: | Existing methods to improve pre-trained language models for many-class classification suffer from verbalizer ambiguity . a significant disparity exists between the pre-training and fine-tuning stages of the model . |
| Approach: | They propose a method to tune pre-trained language models to a broad spectrum of tasks . they use an instance-dependent soft prefix to complement language verbalizers in many-class classification . |
| Outcome: | The proposed method outperforms baselines on many-class datasets. |
Towards Robust Ranker for Text Retrieval (2023.findings-acl)
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| Challenge: | Existing methods for text retrieval are based on a 'retrieval & rerank' pipeline, which uses a fast retriever to fetch a set of top document candidates, while a robust ranker is based upon a weak negative mining during contrastive learning. |
| Approach: | They propose a multi-adversarial training strategy that leverages multiple retrievers as generators to challenge a ranker. |
| Outcome: | The proposed model outperforms the existing de facto ranker training paradigms on the passage retrieval benchmarks using BM25-reranking, full-ranking and retriever distillation. |
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion (2020.coling-main)
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| Challenge: | Existing approaches for knowledge graph embedding have limitations in complex vector space . embeddability of one-to-many relations is not explicitly alleviated . |
| Approach: | They propose a relation-adaptive translating embedding function that can be extended to complex vector space. |
| Outcome: | The proposed translation function improves expressive power and alleviates embedding ambiguity problem. |
Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)
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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations. |
| Approach: | They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference. |
| Outcome: | The proposed method significantly mitigates object hallucinations across various model architectures. |