Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction (P19-1)
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| Challenge: | Current relation extraction methods suffer from noisy labels and incomplete knowledge base information. |
| Approach: | They propose a pre-trained language model that captures semantic and syntactic features and a significant amount of “common-sense” knowledge. |
| Outcome: | The proposed model achieves state-of-the-art AUC score of 0.422 on the NYT10 dataset and performs especially well at higher recall levels. |
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Deep Bidirectional Transformers for Relation Extraction without Supervision (D19-61)
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| Challenge: | Existing frameworks for relation extraction use distant supervision instead of annotated data. |
| Approach: | They propose a framework to deal with relation extraction tasks without supervision . they use syntactic parsing and pre-trained word embeddings to extract relations . |
| Outcome: | The proposed framework outperforms baselines on four biomedical datasets and achieves slightly worse results than the state-of-the-art in three out of four data sets. |
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing. |
| Approach: | They build a dataset using DS-generated data as training data and hire annotators to label test data. |
| Outcome: | The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation. |
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors (2021.naacl-main)
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| Challenge: | Existing methods to facilitate distantly supervised relation extraction are noisy instances, long-tail relations and unbalanced bag sizes. |
| Approach: | They propose a multi-task approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. |
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GPT-RE: In-context Learning for Relation Extraction using Large Language Models (2023.emnlp-main)
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| Challenge: | Existing approaches to in-context learning (ICL) are lacking in relation extraction (RE) . emergence of large language models (LLMs) such as GPT-3 represents a significant advancement in natural language processing. |
| Approach: | They propose to incorporate task-aware representations into demonstration retrieval and enrich the demonstrations with gold label-induced reasoning logic. |
| Outcome: | The proposed model achieves SOTA and competitive performances on the Semeval and SciERC datasets. |
Combining Distant and Direct Supervision for Neural Relation Extraction (N19-1)
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| Challenge: | Existing methods to train relation extraction with distant supervision use noisy labels and implicitly assumes that all the KB facts are mentioned in the text. |
| Approach: | They propose to combine distant supervision data with additional directly-supervised data to train relation extraction models by using sigmoidal attention weights with max pooling. |
| Outcome: | The proposed method achieves state-of-the-art on the widely used FB-NYT dataset. |
Distantly Supervised Relation Extraction in Federated Settings (2021.findings-emnlp)
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| Challenge: | Existing methods to label training datasets using distant supervision are expensive and cannot cover all walks of life. |
| Approach: | They propose a federated denoising framework to suppress label noise in federation . they propose to use a multiple instance learning based denoisation method to select reliable sentences . |
| Outcome: | The proposed method can select reliable sentences via cross-platform collaboration. |
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning (D18-1)
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| Challenge: | Existing methods for extracting relations are slow and lack precision . a novel approach to extract relations is proposed to reduce noise between sentences . |
| Approach: | They propose a word-level distant supervised approach for relation extraction using New York Times and Freebase. |
| Outcome: | The proposed method improves the area of precision/call(PR) from 0.35 to 0.39 over the state-of-the-art methods. |
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)
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| Challenge: | Distantly-supervised Relation Extraction (RE) methods ignore readily available side information. |
| Approach: | They propose a distantly-supervised neural relation extraction method which uses additional side information from KBs to train an extractor. |
| Outcome: | The proposed method improves performance even when limited side information is available. |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
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| Challenge: | Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean. |
| Approach: | They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels. |
| Outcome: | The proposed framework improves the current art in uncovering the ground-truth relation labels. |
Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction (2022.acl-srw)
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| Challenge: | Existing models that perform explicit on-task training of graph embeddings are inadequate. |
| Approach: | They propose to combine pretrained knowledge base graph embeddings with transformer based language models to improve performance on sentential Relation Extraction task. |
| Outcome: | The proposed model outperforms state-of-the-art models on the sentential Relation Extraction task. |