Papers with relation classifier
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. |
| Outcome: | The proposed approach improves performance on two datasets created via distant supervision. |
Bootstrapping Neural Relation and Explanation Classifiers (2023.acl-short)
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| Challenge: | supervised approaches that use only rules to explain the outputs of the relation classifier are data hungry and expensive to obtain. |
| Approach: | They propose a method that self trains (or bootstraps) neural relation and explanation classifiers by iterating the outputs into rules and applying them to unlabeled text to produce new annotations. |
| Outcome: | The proposed method outperforms the rule-based model on the TACRED dataset by 15 F1 points and performs comparatively with the prompt-based approach without an additional natural language inference component. |
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction (D18-1)
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| Challenge: | Existing methods to extract genre-specific and genre-agnostic features require great human effort. |
| Approach: | They propose to use two encoders to explicitly extract genre-specific and genre-agnostic features. |
| Outcome: | The proposed approach outperforms the state-of-the-art by 1.7% on three distinct genres. |
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities. |
| Approach: | They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier. |
| Outcome: | The proposed method performs well in the current distant supervision dataset. |
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision (N19-1)
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| Challenge: | Existing methods to train unbiased methods such as REINFORCE take time to train. |
| Approach: | They propose to use posterior regularization to integrate domain-specific rules in instance selection using REINFORCE to improve the performance of the relation classifier trained on cleaned distant supervision datasets. |
| Outcome: | The proposed method improves the performance of the relation classifier trained on cleaned distant supervision dataset as well as the efficiency of the REINFORCE training. |
Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation (2023.acl-long)
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| Challenge: | Discourse connectives are words or phrases that signal the presence of a discourse relation. |
| Approach: | They propose a model that generates discourse connectives between arguments and predicts discourse relations based on the generated connectives. |
| Outcome: | The proposed model outperforms baselines on three datasets and is highly accurate. |