Papers by Wen Hua
TOME: A Two-stage Approach for Model-based Retrieval (2023.acl-long)
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| Challenge: | Recent research has focused on model-based retrieval, which discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
| Approach: | They propose a model-based retrieval approach that discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
| Outcome: | The proposed approach eliminates the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs (2025.findings-emnlp)
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| Challenge: | Existing evaluations for Structured Knowledge (SK) understanding are non-rigorous and focus on a single type of SK. |
| Approach: | They propose a structured knowledge understanding benchmark that includes four widely used structured knowledge forms. |
| Outcome: | The proposed benchmark is based on four widely used structured knowledge forms . it includes a question, an answer, positive knowledge units, and noisy knowledge units . |
Guiding Neural Entity Alignment with Compatibility (2022.emnlp-main)
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| Challenge: | Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs) labelled data is used to learn neural EA models, but this aspect is neglected . |
| Approach: | They propose a framework to integrate compatibility into neural EA models . they aim to find equivalent entities between two Knowledge Graphs (KGs) |
| Outcome: | The proposed framework can achieve comparable effectiveness with supervised training using 20% of labelled data. |
VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods for Document-level Relation Extraction assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. |
| Approach: | They propose a method that leverages the Variational Autoencoder architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations. |
| Outcome: | The proposed method outperforms state-of-the-art models on two benchmark datasets and is available on github. |
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval (2021.findings-acl)
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Ruiyang Ren, Shangwen Lv, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, Ji-Rong Wen
| Challenge: | Recent studies only consider query-centric similarity relation when learning the dual-encoder retriever. |
| Approach: | They propose a query-centric and PAssage-centric approach to capture more comprehensive similarity relations for dense passage retrieval. |
| Outcome: | The proposed approach significantly outperforms existing models on both MSMARCO and Natural Questions datasets. |
Unilaw-R1: A Large Language Model for Legal Reasoning with Reinforcement Learning and Iterative Inference (2025.emnlp-main)
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| Challenge: | Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remain underexplored. |
| Approach: | They propose a large language model tailored for legal reasoning with a 7-billion parameter scale and a two-stage training strategy combining Supervised Fine-Tuning and Reinforcement Learning. |
| Outcome: | The proposed model outperforms all models of similar scale on authoritative benchmarks and outperformed Qwen-2.5-7B-Instruct (46.6%) by an average margin of 6.6%. |
Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)
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Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang
| Challenge: | Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks. |
| Approach: | They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration. |
| Outcome: | The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks. |
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation (2025.coling-main)
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| Challenge: | Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge, but it remains unclear how well they perceive their factual knowledge boundaries. |
| Approach: | They propose to use a retrieval augmentation approach to enhance LLMs' awareness of factual knowledge boundaries to analyze factual and factual information in open-domain question answering (QA) |
| Outcome: | The proposed method improves LLMs’ QA and judgemental capabilities by integrating supporting documents with the questions. |
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking (2021.emnlp-main)
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| Challenge: | Recent studies show that passage retrieval and passage reranking are important for achieving mutual improvement. |
| Approach: | They propose a unified listwise training approach for passage retrieval and passage reranking that incorporates a retrieval procedure and a hybrid data augmentation strategy. |
| Outcome: | The proposed approach improves on both MSMARCO and Natural Questions datasets. |
ActiveEA: Active Learning for Neural Entity Alignment (2021.emnlp-main)
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| Challenge: | Existing approaches to combining knowledge Graphs (KGs) are incomplete but complementary to each other. |
| Approach: | They propose a novel Active Learning framework for neural EA that creates highly informative seed alignments to obtain more effective models with less annotation cost. |
| Outcome: | The proposed framework significantly improves sampling quality with good generality across different datasets, EA models and amount of bachelors. |
RCTEA: Richness-guided Co-training for Temporal Entity Alignment (2026.findings-acl)
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| Challenge: | Existing TEA models fail to capture the orthogonal yet complementary effect between structural and temporal features. |
| Approach: | They propose a framework that jointly models structural and temporal aspects of Temporal Knowledge Graphs for entity alignment. |
| Outcome: | The proposed framework achieves state-of-the-art on public TEA benchmarks. |
A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)
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Ruiyang Ren, Yingqi Qu, Jing Liu, Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, Ji-Rong Wen
| Challenge: | Recent advances in dense retrieval (DR) models have been shown to be not as competitive as traditional sparse retrieval models in a zero-shot retrieval setting. |
| Approach: | They propose to examine the zero-shot capability of DR models by analyzing key factors related to source training set and potential bias from target dataset. |
| Outcome: | The proposed model is not as competitive as sparse retrieval models in a zero-shot retrieval setting. |
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech (2022.acl-long)
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| Challenge: | Experimental results show that the proposed model improves naturalness and prosody diversity with clear margins. |
| Approach: | They propose a cross-utterance conditional VAE to estimate posterior probability distribution of latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features from past and future sentences. |
| Outcome: | The proposed model improves naturalness and prosody diversity with clear margins. |
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents (2024.findings-emnlp)
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| Challenge: | Existing research on web search rely on real-user experiments, which can be costly to scale up. |
| Approach: | They propose a user simulation framework with LLM-based agents that can generate unique user profiles at scale. |
| Outcome: | The proposed framework can generate unique user profiles at scale, leading to diverse search behaviors. |