Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction (D19-1)
Copied to clipboard
| Challenge: | Distantly Supervised Relation Extraction (DSRE) suffers from mislabelled data . human annotation on large datasets is costly and often impossible . |
| Approach: | They propose a model that employs a collaborative curriculum learning framework to reduce mislabelled data. |
| Outcome: | The proposed model outperforms baselines including state-of-the-art in terms of P@N and PR curve metrics on a widely-used public dataset. |
Similar Papers
Combining Distant and Direct Supervision for Neural Relation Extraction (N19-1)
Copied to clipboard
| 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. |
Denoising Relation Extraction from Document-level Distant Supervision (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to generate auto-labeled sentences for relation extraction (RE) are difficult to extend to document-level relation extraction as noise from DS may be even multiplied in documents. |
| Approach: | They propose a pre-trained model which de-emphasizes noisy DS data via multiple pre-training tasks. |
| Outcome: | The proposed model can capture useful information from noisy data and achieve promising results on the large-scale DocRE benchmark. |
Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Distantly supervised relation extraction (DSRE) methods are not capable of extracting relation labels for individual sentences. |
| Approach: | They propose a semi-supervised learning relation extraction framework for sentence-level DSRE . they discard only the labels of the noisy samples and utilize them as unlabeled samples . |
| Outcome: | The proposed framework achieves significant performance enhancements on two real-world datasets. |
Distant Supervised Relation Extraction with Separate Head-Tail CNN (D19-55)
Copied to clipboard
| Challenge: | Distant supervised relation extraction suffers from mislabeling problem due to strong assumption . et al., 2009: distant supervision can be used to extract relations between entities . |
| Approach: | They propose a neural relation extraction framework which uses convolution and pooling to extract better semantic features of sentences. |
| Outcome: | The proposed framework improves relation extraction performance on a widely used dataset. |
SENT: Sentence-level Distant Relation Extraction via Negative Training (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relation extraction use bag labels, which introduce noise, to train the model. |
| Approach: | They propose to use negative training to train a model using complementary labels to separate the noisy data from the training data. |
| Outcome: | The proposed method improves on previous methods on sentence-level evaluation and de-noise effect. |
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches of distantly supervised relation extraction (DSRE) focus on sentence-level or bag-level de-noising, neglecting the explicit interaction with cross levels. |
| Approach: | They propose a hierarchical contrastive learning framework for distantly supervised relation extraction to reduce noisy sentences. |
| Outcome: | The proposed framework outperforms baselines in various mainstream DSRE datasets. |
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)
Copied to clipboard
| 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. |
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors (2021.naacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed approach improves performance on two datasets created via distant supervision. |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
Copied to clipboard
| 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. |
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing systems for question generation based on shallow linguistic information at text level generate questions for reading comprehension. |
| Approach: | They propose a relation extraction system that extracts pedagogically motivated relation types from a text to generate questions for reading comprehension. |
| Outcome: | The proposed system extracts pedagogically motivated relations from a text . it performs better on the subset of relations suitable for pedagical purposes. |