Papers by Haoyu Song
Event Semantic Classification in Context (2024.findings-eacl)
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| Challenge: | In this work, we focus on the semantic classification of events in context to help machines gain a deeper understanding of events. |
| Approach: | They propose to integrate event semantics into downstream tasks to help machines understand events better. |
| Outcome: | The proposed model improves the understanding of events in context. |
CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment (2022.acl-long)
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| Challenge: | Previously, CLIP was only regarded as a powerful visual encoder. |
| Approach: | They propose a parameter-efficient fine-tuning strategy to boost CLIP's few-shot performance on a visual entailment task without introducing any additional pre-training procedure. |
| Outcome: | The proposed strategy achieves competitive zero/few-shot results on visual question answering and visual entailment tasks without introducing any additional pre-training procedure. |
Cognitive Scaffold: From Fluid Context to Crystallized Memory for Long-Horizon DeepResearch Agents (2026.acl-long)
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| Challenge: | Scaling LLM-based agents to long-horizon deep research is constrained by context-noise trade-off . solving a single query may require hundreds of interactions with noisy environments . |
| Approach: | They propose a factorized memory architecture that decouples the cognitive state into a Fluid Working Context for immediate reasoning and a persistent Knowledge Graph for long-term retention. |
| Outcome: | The Cognitive Scaffold outperforms baselines on Xbench-DeepSearch, BrowseComp-ZH, and GAIA . it achieves 74.7% Avg@3 and 87.0% Pass@3 on xbench, browseComp, and 88.3% Pass@3. |
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data (2021.acl-long)
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| Challenge: | Existing persona-based dialogue models use crowd-sourced data, such as the PersonaChat . however, the cost of such datasets is limited, and the model is not robust. |
| Approach: | They propose to disentangle persona-based dialogue generation into two sub-tasks by using a BERT-over-BERT model. |
| Outcome: | The proposed model outperforms baselines in response quality and persona consistency under different limited data settings. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
Profile Consistency Identification for Open-domain Dialogue Agents (2020.emnlp-main)
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| Challenge: | Existing studies on improving attribute consistency focus on incorporating attribute information in responses, but few efforts have identified the consistency relations between response and attribute profile. |
| Approach: | They propose a key-value structure information enriched BERT model to identify the profile consistency . they propose to incorporate attribute information into the generated responses . |
| Outcome: | The proposed model improves over strong baselines on downstream tasks. |
AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction (2026.acl-long)
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Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao, Tianshi Zheng, Baixuan Xu, Shujie Liu, Yangqiu Song
| Challenge: | Currently, knowledge graphs are decoupled from their downstream application, resulting in suboptimal graph structures. |
| Approach: | They propose a framework to directly optimize KG construction for task performance using Reinforcement Learning (RL). |
| Outcome: | The proposed framework improves performance across multiple QA benchmarks and consistently achieves significant performance gains over task-agnostic baseline graphs. |
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation (2020.acl-main)
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| Challenge: | Existing persona-based dialogue models generate human-like responses but can hardly avoid the generation of inconsistent persona words. |
| Approach: | They propose a framework that deletes inconsistent words from a generated response prototype and further rewrites it to a personality-consistent one. |
| Outcome: | The proposed framework achieves good performance on the persona-chat dataset. |
CEMT:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning (2026.findings-acl)
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| Challenge: | Large Language Models suffer from paraphrasing errors, omissions, or hallucinations when input contains translation-specific elements that require strict preservation or controlled transformation. |
| Approach: | They propose a Controllable Element-Oriented Machine Translation framework that decomposes the translation process into a linguistically grounded analysis, strategy formulation, and final generation. |
| Outcome: | The proposed framework improves on the WMT23/24 Chinese–English benchmarks while significantly reducing element-level constraint violations. |
Analogous Process Structure Induction for Sub-event Sequence Prediction (2020.emnlp-main)
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| Challenge: | Existing work on event understanding is focusing on procedural (or horizontal) tasks such as predicting the next event given an observed sequence. |
| Approach: | They propose an Analogous Process Structure Induction framework which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub- sequence of previously unseen open-domain processes. |
| Outcome: | The proposed framework can predict the whole sub-event sequence of previously unseen open-domain processes. |