Papers with document-level tasks
SPECTER: Document-level Representation Learning using Citation-informed Transformers (2020.acl-main)
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| Challenge: | Recent Transformer language models do not leverage information on inter-document relatedness, which limits their document-level representation power. |
| Approach: | They propose a method to generate document-level embeddings using citation graphs. |
| Outcome: | The proposed method outperforms baselines on document-level tasks. |
Neural Generative Rhetorical Structure Parsing (D19-1)
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| Challenge: | Existing approaches to RST parsing use discriminative models, but these are less sample efficient and datasets are typically small. |
| Approach: | They propose a document-level RNN grammar with a bottom-up traversal order and a beam search algorithm that keeps track of both structure-and word-generating actions without exhibit-ing this branching bias. |
| Outcome: | The proposed model outperforms a discriminative model with the same features by 2.6 F1points and achieves comparable performance to the state-of-the-art. |
ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models (2024.naacl-long)
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| Challenge: | Detecting contradictions in texts is often regarded as determining relation between hypothesis and piece of premise. |
| Approach: | They propose a human-annotated dataset to study self-contradictions in long documents . they analyze the capabilities of four open-source and commercially available LLMs . |
| Outcome: | The proposed dataset outperforms open-source LLMs on document-level tasks but struggles with self-contradictions that require more nuance and context. |
VE-KD: Vocabulary-Expansion Knowledge-Distillation for Training Smaller Domain-Specific Language Models (2024.findings-emnlp)
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| Challenge: | VE-KD is a method that balances knowledge distillation and vocabulary expansion with the aim of training efficient domain-specific language models. |
| Approach: | They propose a method that balances knowledge distillation and vocabulary expansion with the aim of training efficient domain-specific language models. |
| Outcome: | VE-KD outperforms DistilBERT and Adapt-and-Distill in biomedical domain tasks . compared with other methods, it outperformed Distilbert and adapted-and distill . |