| Challenge: | Template filling tasks are usually tackled by a pipeline of two separate systems, one for role-filler extraction and another for template/event recognition. |
| Approach: | They propose a framework that naturally models the dependence between entities within a single event and across multiple events described in a document. |
| Outcome: | The proposed framework outperforms pipeline-based approaches and other neural baselines that do not model between-event dependencies on documents containing multiple events. |
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GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction (2021.eacl-main)
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| Challenge: | Document-level role-filler entity extraction (REE) is a classic problem in information extraction (IE) and NLP. |
| Approach: | They propose a document-level role-filler entity extraction framework that can make extraction decisions across sentence boundaries and implicitly recognizes noun phrase coreference structure. |
| Outcome: | The proposed framework performs better than previous models on a MUC-4 dataset. |
On Event Individuation for Document-Level Information Extraction (2023.findings-emnlp)
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| Challenge: | a bomb exploded in a restaurant in Lima, and a second device was deactivated by the police . |
| Approach: | They argue that the task demands definitive answers to thorny questions of *event individuation* they argue that even human experts disagree on the task . |
| Outcome: | The proposed task demands definitive answers to thorny questions of *event individuation* . the proposed task also raises concerns about the usefulness of template filling metrics . |
Document-level Entity-based Extraction as Template Generation (2021.emnlp-main)
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| Challenge: | Document-level entity-based extraction (EE) tasks extract entity-centric information from unstructured text across multiple sentences. |
| Approach: | They propose a generative framework for two document-level EE tasks: role-filler entity extraction (RE) and relation extraction ( RE). |
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Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template (D19-55)
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| Challenge: | Recent approaches for paraphrasing generate unpredictable results . |
| Approach: | They propose a question paraphrasing pipeline model based on templates that identifies template and retrieves candidate templates and fills them with original topic words. |
| Outcome: | The proposed model outperforms the seq2seq model on two datasets and is more promising when the training sample is small. |
Hierarchical Transformers for Multi-Document Summarization (P19-1)
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| Challenge: | Existing models for multidocument summarization have been developed that can process multiple documents in a hierarchical manner. |
| Approach: | They propose a neural summarization model which can process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. |
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Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction (2020.acl-main)
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| Challenge: | Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. |
| Approach: | They propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. |
| Outcome: | The proposed method outperforms existing methods in the extraction of emotion-cause pairs . it emphasizes inter-clause modeling to perform end-to-end extraction . |
Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025.coling-main)
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| Challenge: | Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis. |
| Approach: | They propose a novel approach to document-level event argument extraction that integrates predefined templates and generative language models into a foundational embedding derived from a classification model. |
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Dynamic Prefix-Tuning for Generative Template-based Event Extraction (2022.acl-long)
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| Challenge: | Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005. |
| Approach: | They propose a generative template-based event extraction method with dynamic prefix . they integrate context information with type-specific prefixes to learn a context-specific name for each context . |
| Outcome: | The proposed method achieves competitive results with state-of-the-art model OneIE on ACE 2005 and performs well on ERE. |
Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm (2024.findings-acl)
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| Challenge: | Document-level event extraction aims to extract structured information from unstructured text. |
| Approach: | They propose a cross-document event extraction pipeline that integrates event information from multiple documents and provides a comprehensive perspective on events. |
| Outcome: | The proposed pipeline achieves about 72% F1 in end-to-end cross-document event extraction, setting up a benchmark for future research. |
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding (2020.acl-main)
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| Challenge: | Document-level event extraction requires a view of a larger context to determine which spans of text correspond to event role fillers. |
| Approach: | They propose a multi-granularity reader to dynamically aggregate information captured by neural representations learned at different levels of granularities. |
| Outcome: | The proposed model performs substantially better than previous models on the MUC-4 event extraction dataset. |