Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction

• URL: http://arxiv.org/abs/2406.11429v1
• Date: Mon, 17 Jun 2024 11:31:48 GMT
• Title: Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction
• Authors: Shilong Li, Ge Bai, Zhang Zhang, Ying Liu, Chenji Lu, Daichi Guo, Ruifang Liu, Yong Sun,
• Abstract summary: We propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost. Experimental results show that our approach outperforms the previous State Of The Art (SOTA) methods.
• Score: 9.657656957845987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting unseen relations that cannot be observed during the training phase is a challenging task in relation extraction. Previous works have made progress by matching the semantics between input instances and label descriptions. However, fine-grained matching often requires laborious manual annotation, and rich interactions between instances and label descriptions come with significant computational overhead. In this work, we propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost, and fuses coarse-grained recall and fine-grained classification for rich interactions with guaranteed inference speed. Experimental results show that our approach outperforms the previous State Of The Art (SOTA) methods, and achieves a balance between inference efficiency and prediction accuracy in zero-shot relation extraction tasks. Our code is available at https://github.com/longls777/EMMA.

Related papers

• FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition [57.17966905865054]
  Real-life applications of action recognition often require a fine-grained understanding of subtle movements. Existing semi-supervised action recognition has mainly focused on coarse-grained action recognition. We propose an Alignability-Verification-based Metric learning technique to effectively discriminate between fine-grained action pairs.
  arXiv  Detail & Related papers  (2024-09-02T20:08:06Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network [48.912196729711624]
  Few-shot semantic segmentation is the task of learning to locate each pixel of a novel class in a query image with only a few annotated support images. We propose a Feature-Enhanced Context-Aware Network (FECANet) to suppress the matching noise caused by inter-class local similarity. In addition, we propose a novel correlation reconstruction module that encodes extra correspondence relations between foreground and background and multi-scale context semantic features.
  arXiv  Detail & Related papers  (2023-01-19T16:31:13Z)
• FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment [93.09267863425492]
  We argue that understanding both high-level semantics and internal temporal structures of actions in competitive sports videos is the key to making predictions accurate and interpretable. We construct a new fine-grained dataset, called FineDiving, developed on diverse diving events with detailed annotations on action procedures.
  arXiv  Detail & Related papers  (2022-04-07T17:59:32Z)
• MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction [11.821464352959454]
  We propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction. We show that incorporating the above two types of mapping information in both pretraining and fine-tuning can significantly improve the model performance.
  arXiv  Detail & Related papers  (2021-09-09T09:02:23Z)
• Representation Learning for Weakly Supervised Relation Extraction [19.689433249830465]
  In this thesis, we present several novel unsupervised pre-training models to learn the distributed text representation features. The experiments have demonstrated that this type of feature, combine with the traditional hand-crafted features, could improve the performance of the logistic classification model for relation extraction.
  arXiv  Detail & Related papers  (2021-04-10T12:22:25Z)
• Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection [69.2370349274216]
  Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Semantic components are distilled from utterances via multi-head self-attention. Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
  arXiv  Detail & Related papers  (2020-10-06T05:16:38Z)
• Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network [83.64416937454801]
  Job-resume interaction data is sparse and noisy, which affects the performance of job-resume match algorithms. We propose a novel multi-view co-teaching network from sparse interaction data for job-resume matching. Our model is able to outperform state-of-the-art methods for job-resume matching.
  arXiv  Detail & Related papers  (2020-09-25T03:09:54Z)
• A Practical Framework for Relation Extraction with Noisy Labels Based on Doubly Transitional Loss [14.121872633596452]
  We introduce a practical end-to-end deep learning framework for automatic labeling. One transition is parameterized by a non-linear transformation between hidden layers. Another is an explicit probability transition matrix that captures the direct conversion between labels.
  arXiv  Detail & Related papers  (2020-04-28T19:38:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.