Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery

• URL: http://arxiv.org/abs/2401.06327v1
• Date: Fri, 12 Jan 2024 02:38:55 GMT
• Title: Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery
• Authors: Jiaxin Wang, Lingling Zhang, Jun Liu, Tianlin Guo, Wenjun Wu
• Abstract summary: Generalized Relation Discovery (GRD) aims to identify unlabeled instances in existing pre-defined relations or discover novel relations. We propose a novel framework, SFGRD, for this task by learning from semi-factuals in two stages. SFGRD surpasses state-of-the-art models in terms of accuracy by 2.36% $sim$5.78% and cosine similarity by 32.19%$sim$ 84.45%.
• Score: 12.716874398564482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. GRD aims to identify unlabeled instances in existing pre-defined relations or discover novel relations by assigning instances to clusters as well as providing specific meanings for these clusters. The key challenges of GRD are how to mitigate the serious model biases caused by labeled pre-defined relations to learn effective relational representations and how to determine the specific semantics of novel relations during classifying or clustering unlabeled instances. We then propose a novel framework, SFGRD, for this task to solve the above issues by learning from semi-factuals in two stages. The first stage is semi-factual generation implemented by a tri-view debiased relation representation module, in which we take each original sentence as the main view and design two debiased views to generate semi-factual examples for this sentence. The second stage is semi-factual thinking executed by a dual-space tri-view collaborative relation learning module, where we design a cluster-semantic space and a class-index space to learn relational semantics and relation label indices, respectively. In addition, we devise alignment and selection strategies to integrate two spaces and establish a self-supervised learning loop for unlabeled data by doing semi-factual thinking across three views. Extensive experimental results show that SFGRD surpasses state-of-the-art models in terms of accuracy by 2.36\% $\sim$5.78\% and cosine similarity by 32.19\%$\sim$ 84.45\% for relation label index and relation semantic quality, respectively. To the best of our knowledge, we are the first to exploit the efficacy of semi-factuals in relation extraction.

Related papers

• Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis [53.38518232934096]
  Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. We propose an MTL approach at the intersection between task clustering and feature transformation based on a two-phase iterative aggregation of targets and features. In both phases, a key aspect is to preserve the interpretability of the reduced targets and features through the aggregation with the mean, which is motivated by applications to Earth science.
  arXiv  Detail & Related papers  (2024-06-12T08:30:16Z)
• Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation [50.407071700154674]
  We propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL) Our work was motivated by that, with the prosperity of computational pathology, an increasing number of fully-annotated datasets are publicly accessible. Extensive experiments on a couple of publicly accessible datasets demonstrate that SGFSIS can outperform other annotation-efficient learning baselines.
  arXiv  Detail & Related papers  (2024-02-26T03:49:18Z)
• Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction [33.528688487954454]
  Relation extraction (RE) aims to identify semantic relationships between entities within text. Few-shot learning, aiming to lessen annotation demands, typically provides incomplete and biased supervision for target relations. We introduce REPaL, comprising three stages: (1) We leverage large language models (LLMs) to generate initial seed instances from relation definitions and an unlabeled corpus.
  arXiv  Detail & Related papers  (2024-02-17T00:20:06Z)
• Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction [4.7220779071424985]
  Few-shot Relation Extraction (FSRE) aims to extract facts from a sparse set of labeled corpora. Recent studies have shown promising results in FSRE by employing Pre-trained Language Models. We introduce a novel synergistic anchored contrastive pre-training framework.
  arXiv  Detail & Related papers  (2023-12-19T10:16:24Z)
• Co-guiding for Multi-intent Spoken Language Understanding [53.30511968323911]
  We propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the mutual guidances between the two tasks. For the first stage, we propose single-task supervised contrastive learning, and for the second stage, we propose co-guiding supervised contrastive learning. Experiment results on multi-intent SLU show that our model outperforms existing models by a large margin.
  arXiv  Detail & Related papers  (2023-11-22T08:06:22Z)
• Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation [13.418617500641401]
  We propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction. We construct a unified span-based heterogeneous graph from entity and relation candidates and propagate class labels based on confidence scores. We show that our framework outperforms the state-of-the-art semi-supervised approaches on NER and RE tasks.
  arXiv  Detail & Related papers  (2023-05-25T09:07:04Z)
• Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation [98.51938442785179]
  Incremental few-shot semantic segmentation aims to incrementally extend a semantic segmentation model to novel classes. This task faces a severe semantic-aliasing issue between base and novel classes due to data imbalance. We propose the Semantic-guided Relation Alignment and Adaptation (SRAA) method that fully considers the guidance of prior semantic information.
  arXiv  Detail & Related papers  (2023-05-18T10:40:52Z)
• Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation [3.154631846975021]
  In relation triplet extraction, recognizing unseen relations for which there are no training instances is a challenging task. We propose a prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize unseen relations under the zero-shot setting.
  arXiv  Detail & Related papers  (2021-12-08T19:34:27Z)
• Deep Relational Metric Learning [84.95793654872399]
  This paper presents a deep relational metric learning framework for image clustering and retrieval. We learn an ensemble of features that characterizes an image from different aspects to model both interclass and intraclass distributions. Experiments on the widely-used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate that our framework improves existing deep metric learning methods and achieves very competitive results.
  arXiv  Detail & Related papers  (2021-08-23T09:31:18Z)
• R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching [58.72111690643359]
  We propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. We first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task.
  arXiv  Detail & Related papers  (2020-12-16T13:11:30Z)
• Clustering-based Unsupervised Generative Relation Extraction [3.342376225738321]
  We propose a Clustering-based Unsupervised generative Relation Extraction framework (CURE) We use an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information. Our model performs better than state-of-the-art models on both New York Times (NYT) and United Nations Parallel Corpus (UNPC) standard datasets.
  arXiv  Detail & Related papers  (2020-09-26T20:36:40Z)

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.