Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder (P18-1)
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| Challenge: | Embedding models for entities and relations are useful for recovering missing facts in knowledge bases. |
| Approach: | They propose a dimension reduction technique by training relations jointly with an autoencoder to capture compositional constraints. |
| Outcome: | The proposed model improves on Knowledge Base Completion tasks with a significantly higher mean rank and better compositional training. |
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Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)
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| Challenge: | Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs). |
| Approach: | They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets. |
Extracting Entities and Relations with Joint Minimum Risk Training (D18-1)
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| Challenge: | Existing methods for detecting entities and relations are limited by the complexity of the joint learning paradigm. |
| Approach: | They propose a joint learning paradigm based on minimum risk training . they implement a strong and simple neural network to execute the MRT . |
| Outcome: | The proposed model is able to achieve state-of-the-art in the extraction task on ACE05 and NYT datasets. |
A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
| Approach: | They propose a pipelined approach for entity and relation extraction that uses two independent encoders to construct the relation model. |
| Outcome: | The proposed approach achieves an 8.16 speedup with a slight reduction in accuracy on standard benchmarks. |
Unsupervised Relation Extraction: A Variational Autoencoder Approach (2021.emnlp-main)
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| Challenge: | Existing methods for relation extraction use latent variables and supervised training which requires large datasets. |
| Approach: | They propose a VAE-based unsupervised relation extraction technique that uses latent variables as an intermediate variable instead of a latent variable. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the NYT dataset and outperformed existing methods. |
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
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Relation Discovery with Out-of-Relation Knowledge Base as Supervision (N19-1)
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| Challenge: | Existing methods to extract relations from text corpus without annotated data are violated by up to 31%. |
| Approach: | They propose to use out-of-relation knowledge bases to supervise the discovery of unseen relations where relations to discover from the text corpus and those in knowledge bases are not overlapped. |
| Outcome: | The proposed method improves the state-of-the-art relation discovery performance by a large margin. |
GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction (2021.findings-emnlp)
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| Challenge: | Existing models for extracting relation triplets suffer from incompletion and disorder problems when they extract multi-token entities from input sentences. |
| Approach: | They propose a special entity labelling method that fine-tunes the pre-trained model and learns the special entity labels simultaneously. |
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ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
| Approach: | They propose a pre-training method to improve the joint extraction performance with just extra entity annotations. |
| Outcome: | The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks. |
Learning Relational Representations by Analogy using Hierarchical Siamese Networks (N19-1)
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| Challenge: | Existing approaches to learn representations of relations by textual mentions require a large amount of examples for each relation to reach satisfactory performance. |
| Approach: | They propose a method to learn representations of relations expressed by their textual mentions by matching triples in knowledge bases with web-scale corpora through distant supervision. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on a relation extraction task. |
Matching the Blanks: Distributional Similarity for Relation Learning (P19-1)
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| Challenge: | Efforts to build general purpose relation extractors that can model arbitrary relations are limited in their ability to generalize. |
| Approach: | They propose to build task-agnostic relation representations solely from entity-linked text to extend Harris’ distributional hypothesis to relations. |
| Outcome: | The proposed representations outperform previous methods on SemEval 2010 Task 8, KBP37, and TACRED even without using any of the task’s training data. |