Papers with document-level task
FALTE: A Toolkit for Fine-grained Annotation for Long Text Evaluation (2022.emnlp-demos)
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| Challenge: | Existing tools to evaluate long text outputs are lacking in the field of NLP . human rating and error analysis remains a crucial component for any evaluation of long text generation. |
| Approach: | They propose a web-based toolkit to collect fine-grained error annotations for long texts . they use a taxonomy to identify errors and assign them to text spans . |
| Outcome: | The proposed tool can be used to evaluate the coherence of long generated summaries. |
Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need (2020.coling-main)
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| Challenge: | Existing work on charge-side representations but not much effort has been made in improving fact-side models. |
| Approach: | They propose to use sentence-level charge labels as an auxiliary task coupled with the main task of document-level charging identification in a multi-task learning framework to improve fact-side representations. |
| Outcome: | The proposed model outperforms a large number of baselines on a document-level charge identification task. |
R2F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference (2022.emnlp-main)
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| Challenge: | Document-level natural language inference (DOCNLI) is a new task in natural language processing. |
| Approach: | They propose a document-level natural language inference framework that fuses sentence-level tasks into a set of sentence-based tasks. |
| Outcome: | The proposed framework improves interpretability and performance with evidence. |
Knowledge-Driven Cross-Document Relation Extraction (2024.findings-acl)
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| Challenge: | Existing approaches to extract relationships between entities are based on sentence-level tasks, but they do not consider domain knowledge, which are assumed to be known to the reader when documents are authored. |
| Approach: | They propose to embed domain knowledge of entities with input text for cross-document RE by embedding domain knowledge with the document. |
| Outcome: | The proposed framework offers interpretability by producing explanatory text for predicted relations between entities and improves performance over baseline methods. |
Adaptive Hinge Balance Loss for Document-Level Relation Extraction (2023.findings-emnlp)
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| Challenge: | Document-Level Relation Extraction is a multi-label classification task . however, most entity pairs do not express any relations . |
| Approach: | They propose to use a distance between the classification threshold and predicted score to reduce the imbalance problem by balancing the easy negatives. |
| Outcome: | The proposed method is superior to other methods, the authors show . it reduces the threshold to an appropriate value, and increases quadratically with the number of entities. |