Papers by Xiaobo Guo
Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction (2025.coling-main)
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| Challenge: | Event Argument Extraction is a critical subtask of Event Extraction, focused on identifying event arguments within text. |
| Approach: | They propose a Fusion Selection-Generation-Based Approach that merges selective and generative methods to enhance argument extraction accuracy. |
| Outcome: | The proposed method improves on the RAMS and WikiEvents, while preserving the unique characteristics of both methods. |
RotateCT: Knowledge Graph Embedding by Rotation and Coordinate Transformation in Complex Space (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding methods fail to model non-commutative composition patterns . Existing methods are limited to complex space, resulting in a large number of parameters. |
| Approach: | They propose a knowledge graph embedding method that transforms the coordinates of each entity and then represents each relation as a rotation from head entity to tail entity in complex space. |
| Outcome: | The proposed method outperforms state-of-the-art methods on link prediction and path query answering. |
From What to Why: Improving Relation Extraction with Rationale Graph (2021.findings-acl)
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| Challenge: | Existing neural relation extraction models are limited by entity type and textual context. |
| Approach: | They propose a novel RAtionale Graph to organize co-occurrence constraints among entity types, triggers and relations in a holistic graph view. |
| Outcome: | The proposed method outperforms baselines significantly and achieves state-of-the-art performance on document-level and sentence-level RE benchmarks. |
Recontextualizing Revitalization: A Mixed Media Approach to Reviving the Nüshu Language (2025.emnlp-main)
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| Challenge: | Nüshu is an endangered language from Jiangyong County, Hunan, China, and the world’s only known writing system created and used exclusively by women. |
| Approach: | They propose to use NüshuStrokes to record all 397 Unicode Nü Shu characters in sequential handwriting by an expert calligrapher. |
| Outcome: | Evaluating five state-of-the-art Chinese Optical Character Recognition systems on NüshuVision lowers CER to 0.67, a modest but meaningful improvement over previous datasets. |
Length Does Matter: Summary Length can Bias Summarization Metrics (2023.emnlp-main)
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| Challenge: | Existing summarization metrics favor shorter or longer summaries, but evaluations of these metrics are flawed. |
| Approach: | They propose a Bayesian normalization technique that effectively diminishes this bias. |
| Outcome: | The proposed method significantly improves the concordance between human annotators and most metrics in terms of summary coherence. |
The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discourse (2024.emnlp-main)
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| Challenge: | enhancing the quality of online public discourse requires promoting foundational human virtues, such as “intellectual humility” (IH) . discourse on social media rewards forgetting our virtuous selves, embedding users within echo chambers and causing negative affect towards those who hold different beliefs. |
| Approach: | They propose to use a codebook to measure "intellectual humility" they manually validated the codebook and used it to develop LLM-based models . |
| Outcome: | The proposed model achieves a Macro-F1 score of 0.64 across labels and 0.70 when predicting IH/IA/Neutral at the coarse level. |
Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts (2024.findings-emnlp)
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| Challenge: | Current research focuses on predefined aspects within structured texts, neglecting complexities of dynamic and disordered environments. |
| Approach: | They propose a benchmark for dynamic aspect-based summarization tailored to unstructured text. |
| Outcome: | The proposed benchmark addresses the complexities of dynamic and disordered environments in unstructured text. |
Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning (2026.eacl-long)
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| Challenge: | scalability challenges arise as conversations extend over weeks or months . current approaches fragment conversations into isolated embeddings or graph representations . |
| Approach: | They propose a working memory framework that actively constructs structured memory representations . the framework organizes conversational fragments into episodic narratives based on momentum . |
| Outcome: | Amory improves performance on LOCOMO benchmark while reducing response time by 50%. |
Document-level Relation Extraction with Dual-tier Heterogeneous Graph (2020.coling-main)
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| Challenge: | Existing methods focus on extracting relations from single sentence . document-level relation extraction requires a comprehension of the whole document . |
| Approach: | They propose a graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level relation extraction. |
| Outcome: | The proposed model achieves state-of-the-art performance on two widely used datasets. |
Capturing Topic Framing via Masked Language Modeling (2022.findings-emnlp)
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| Challenge: | a framework for measuring differential framing of issues is needed to address these issues . issue framers can be expressed explicitly with evaluative language or implicitly . quantitative methods have been used to measure issue framming . |
| Approach: | They propose a framework for modeling the differential framing of issues through masked token prediction using large-scale fine-tuned language models. |
| Outcome: | The proposed framework captures differential framing of issues with high reliability . it can be used to predict tone and word choices in written language . |
Serial Position Effects of Large Language Models (2025.findings-acl)
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| Challenge: | Serial position effects (SPE) are well-documented cognitive biases in human behavior. |
| Approach: | They propose to use binary choices instead of multiple choices where feasible . they also suggest limiting prompt length and placing crucial information at the beginning of prompts . |
| Outcome: | The proposed framework shows that the effects are widespread across LLMs and the proposed mitigation methods are effective. |
MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization (2024.findings-acl)
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| Challenge: | Existing methods for generating content specific summarization assume a fixed set of known aspects. |
| Approach: | They propose a dynamic aspect-based summarization framework that optimizes aspect number prediction and minimizes disparity between generated and reference summaries. |
| Outcome: | The proposed method outperforms baselines on three diverse datasets on different aspects of the input text. |