Papers by Shaoru Guo
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Existing methods for machine reading comprehension rely on manually defined features and are difficult to generalize to other tasks. |
| Approach: | They propose a Syntax and Frame Semantics model for Machine Reading Comprehension which takes full advantage of syntax and frame semantics to get richer text representation. |
| Outcome: | The proposed model outperforms ten state-of-the-art models on machine reading comprehension tasks. |
A Frame-based Sentence Representation for Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing machine learning approaches do not have above semantic knowledge to address complicated MRC questions. |
| Approach: | They propose a frame-based Sentence Representation method which integrates frame semantic knowledge to facilitate sentence modelling. |
| Outcome: | The proposed method performs better than state-of-the-art methods on machine reading comprehension task. |
EventOA: An Event Ontology Alignment Benchmark Based on FrameNet and Wikidata (2023.findings-acl)
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| Challenge: | Existing studies on event ontologies focus on entity-based OA, and neglect event-based one . however, independent development of event ontoologies often results in heterogeneous representations that raise the need for establishing alignments between semantically related events. |
| Approach: | They propose a multi-view event ontology alignment method that utilizes description information and neighbor information to obtain richer representations of the event ontoologies. |
| Outcome: | The proposed method outperforms existing entity-based methods and can serve as a strong baseline for future research. |
Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization (2021.emnlp-main)
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| Challenge: | Existing graph-based methods only consider word relations or structure information, which neglect the correlation between them. |
| Approach: | They propose a Dual Graph network for Abstractive Sentence Summarization that captures word relations and structure information from sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two popular benchmark datasets. |
UERLens: Understanding Event Relations in Large Language Models (2026.acl-short)
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| Challenge: | Existing studies on event relation extraction (ERE) have focused on improving model performance. |
| Approach: | They propose an interpretability framework for understanding event relations in large language models . they first construct a counterfactual dataset that includes causal, temporal, and sub-event relations . |
| Outcome: | The proposed framework improves event relation extraction by leveraging internal features to train a lightweight classifier. |
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization (2021.emnlp-main)
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| Challenge: | Sentence-level extractive text summarization is difficult to model the importance of sentences. |
| Approach: | They propose a Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization that leverages Frame semantics to model sentences from both intra-sentence level and inter-sentent level. |
| Outcome: | The proposed model outperforms six state-of-the-art methods on two benchmark corpus datasets. |
NutFrame: Frame-based Conceptual Structure Induction with LLMs (2024.lrec-main)
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| Challenge: | Existing studies focus on syntactic knowledge and world knowledge, but conceptual structure is not well-understood. |
| Approach: | They propose a benchmark for coNceptual structure induction based on FrameNet . they use prompts to induce conceptual structure of Framenet with LLMs . |
| Outcome: | The proposed model is able to induce conceptual structure of FrameNet with LLMs. |