| Challenge: | Using annotated sentences and document-level UDS graphs, we induce an event structure classification with semantic role, entity, and event-event relation classifications. |
| Approach: | They propose to use Universal Decompositional Semantics (UDS) graphs to induce event structure classification . they augment existing annotations with inferential properties capturing fine-grained aspects of temporal and aspectual structure of events. |
| Outcome: | The proposed model is the largest annotation of event structure and (partial) event coreference to date. |
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Event-Centric Natural Language Processing (2021.acl-tutorials)
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| Challenge: | This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations. |
| Approach: | This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks. |
| Outcome: | This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks. |
The Universal Decompositional Semantics Dataset and Decomp Toolkit (2020.lrec-1)
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Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, Sheng Zhang, Francis Ferraro, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
| Challenge: | Decompositional semantics is a method of crowd-sourcing semantic annotations while retaining high interannotator agreement. |
| Approach: | They present the Universal Decompositional Semantics dataset (v1.0) they propose a decomposition-aligned approach to semantic annotation that uses simple questions to answer . |
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Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)
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| Challenge: | Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources. |
| Approach: | They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages. |
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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. |
Event-Keyed Summarization (2024.findings-emnlp)
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| Challenge: | a novel task combines document-level event extraction with event-keyed summarization . a recent study has shown that traditional summarizing produces inferior summaries of target events . |
| Approach: | They propose a task that marries traditional summarization and document-level event extraction with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure. |
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Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)
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| Challenge: | Existing approaches to event extraction are limited to a set of pre-defined types. |
| Approach: | They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text. |
| Outcome: | The proposed framework outperforms existing methods on zero-shot event extraction. |
Integrating Generative Lexicon Event Structures into VerbNet (L18-1)
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| Challenge: | Efforts to use the verb lexicon's semantic representations have revealed a need to revise the form to allow for greater flexibility in representing complex events. |
| Approach: | They propose to restrict the form to first-order representations to simplify use by planners and integrate with the Generative Lexicon's event structure. |
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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. |
Fine-Grained Temporal Relation Extraction (P19-1)
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| Challenge: | Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events. |
| Approach: | They propose a semantic framework for temporal relations and event durations that maps pairs of events to real-valued scales. |
| Outcome: | The proposed framework can predict fine-grained temporal relations and event durations . it can be applied to the entire English Web Treebank dataset . |
Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)
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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin
| Challenge: | Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art. |
| Approach: | They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data. |
| Outcome: | The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art. |