| Challenge: | a problem faced by conversational agents working with large documents is the frequent presence of information that is irrelevant to the agent. |
| Approach: | They propose a neural model for scoping relevant information from a large document . they show that the model performs better with emails than existing baselines . |
| Outcome: | The proposed model improves intent detection and entity extraction tasks without drop in recall. |
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| Challenge: | Reading comprehension (RC) is a high-level task in natural language understanding that requires reading a document and answering questions about its content. |
| Approach: | They propose to provide a standard neural network for reading a document and answering a question about its content. |
| Outcome: | The proposed model improves on the competitive SQuAD dataset by providing rich contextualized word representations and allowing it to choose between context-dependent and context-independent representations. |
SciREX: A Challenge Dataset for Document-Level Information Extraction (2020.acl-main)
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| Challenge: | Conventional datasets and methods for information extraction focus on within-sentence relations from general Newswire text. |
| Approach: | They propose a document-level IE dataset that integrates automatic and human annotations to annotate entities and document- level N-ary relation identification from scientific articles. |
| Outcome: | The proposed dataset extends state-of-the-art IE models to document-level IE. |
Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction (2022.naacl-main)
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| Challenge: | Existing document-level relation extraction methods do not distinguish between mention-level features and entity-level feature . document-based methods are more challenging because of multiple mentions of entities. |
| Approach: | They propose a method which selectively attentions different entity mentions with respect to candidate relations and performs relation-specific representations of entities. |
| Outcome: | The proposed method improves relation-specific representations of entities on two benchmark datasets. |
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing (2022.naacl-main)
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| Challenge: | Document-level relation extraction models are not robust and exhibit bizarre behaviors when non-evidence sentences are removed. |
| Approach: | They propose a document-level relation extraction framework that uses a sentence importance score and a focusing loss to encourage DocRE models to focus on evidence sentences. |
| Outcome: | The proposed framework improves overall performance and makes DocRE models more robust. |
AutoRE: Document-Level Relation Extraction with Large Language Models (2024.acl-demos)
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| Challenge: | Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks. |
| Approach: | They propose an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. |
| Outcome: | The proposed model surpasses TAG by 10.03% and 9.03% on the dev and test set. |
Rethinking Document-Level Relation Extraction: A Reality Check (2023.findings-acl)
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| Challenge: | Recent efforts push up performance boundaries of document-level relation extraction (DocRE) but these efforts are not promising. |
| Approach: | They construct four types of entity mention attacks to examine model robustness . they also have a close check on model usability in a more realistic setting . |
| Outcome: | The proposed model is based on a strong or untenable assumption in common . the model is robust under four types of mention attacks and usable in a realistic setting . |
From What to Why: Improving Relation Extraction with Rationale Graph (2021.findings-acl)
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| Challenge: | Existing neural relation extraction models are limited by entity type and textual context. |
| Approach: | They propose a novel RAtionale Graph to organize co-occurrence constraints among entity types, triggers and relations in a holistic graph view. |
| Outcome: | The proposed method outperforms baselines significantly and achieves state-of-the-art performance on document-level and sentence-level RE benchmarks. |
Pointing Out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials (2024.lrec-main)
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| Challenge: | Recent large language models have achieved state-of-the-art performance on many NLP tasks, but they rely on shortcut features and are unreliable when put under pressure. |
| Approach: | They propose to use semantically-motivated strategies to generate adversarial examples by replacing entity mentions to generate relation extraction models. |
| Outcome: | The proposed models show a lack of robustness when put under pressure. |
Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
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| Challenge: | Relation extraction (RE) aims to identify the semantic relations between named entities in text. |
| Approach: | They propose a novel relation extraction model that encodes document information in terms of entity global and local representations and context relation representations. |
| Outcome: | The proposed model achieves superior performance on two public datasets for document-level RE. |
Relation Extraction with Explanation (2020.acl-main)
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| Challenge: | Recent studies focus on improving relation extraction accuracy but little is known about their explanability. |
| Approach: | They propose to automatically generate "distractor" sentences to augment the bags and train the model to ignore the distractors. |
| Outcome: | The proposed model improves extraction accuracy while also explanability. |