Papers with sentence-level task
Document-Level Event Argument Extraction by Conditional Generation (2021.naacl-main)
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| Challenge: | Existing event extraction models have been limited to the sentence level . this formulation signifies a misalignment between the information seeking behavior and the informative seeking behavior. |
| Approach: | They propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. |
| Outcome: | The proposed model achieves 7.6% F1 and 5.7% F1 over the best baseline on the document-level event extraction dataset WikiEvents and 9.3% F1 on the informative argument extraction task. |
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning (2022.coling-1)
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| Challenge: | Document-level Event Factuality Identification (DEFI) is a fundamental and crucial task in NLP. |
| Approach: | They propose a framework for document-level event factuality identification (DEFI) they propose to use Span-Extraction and Multiple-Choice to model DEFI as machine reading comprehension tasks . |
| Outcome: | The proposed model outperforms state-of-the-art models on a document-based event factuality task . it uses Span-Extraction (Ext) and Multiple-Choice (Mch) knowledge to extract knowledge from large-scale MRC corpus . |
Reconsidering Sentence-Level Sign Language Translation (2024.emnlp-main)
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| Challenge: | Historically, sign language machine translation is framed as a sentence-level task . however, there are known intersentential dependencies that are impossible to resolve in isolation. |
| Approach: | They propose a human baseline for sign language translation that substitutes a person into the machine learning task framing instead of providing the entire document as context. |
| Outcome: | The proposed human baseline for sign language translation shows that deaf signers can only understand key parts of the clip in light of additional discourse-level context. |
Discriminative Reasoning with Sparse Event Representation for Document-level Event-Event Relation Extraction (2023.acl-long)
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| Challenge: | Document-level Event Causality Identification (DECI) is a sentence-level task that requires long-text understanding. |
| Approach: | They propose a document-level event causality identification model (SENDIR) that uses sparse attention to capture long-distance dependence. |
| Outcome: | The proposed model can be used to discriminate between event pairs in the same sentence or span multiple sentences. |