Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training

Wang, Yingyao; Bao, Junwei; Liu, Guangyi; Wu, Youzheng; He, Xiaodong; Zhou, Bowen; Zhao, Tiejun

doi:10.18653/v1/2020.coling-main.510

Cited by 32 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This article compares the BMAN with the following baselines: MLMAN ( Ye & Ling, 2019 ): This approach interactively encodes support instances with query instances, and the matching process strengthens the connection between the query set and the relational prototype. CTEG ( Wang et al, 2020 ): The model uses entity-guided attention mechanisms and confusion-aware training to distinguish between problems of relational confusion. TPN(BERT) ( Wen et al, 2021 ): The method integrates the transformer model into the prototype network and uses the pre-trained BERT as the encoder for the model.…”

Section: Methodsmentioning

confidence: 99%

Bidirectional matching and aggregation network for few-shot relation extraction

Wei

Guo

Zhang

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

Few-shot relation extraction is used to solve the problem of long tail distribution of data by matching between query instances and support instances. Existing methods focus only on the single direction process of matching, ignoring the symmetry of the data in the process. To address this issue, we propose the bidirectional matching and aggregation network (BMAN), which is particularly powerful when the training data is symmetrical. This model not only tries to extract relations for query instances, but also seeks relational prototypes about the query instances to validate the feature representation of the support set. Moreover, to avoid overfitting in bidirectional matching, the data enhancement method was designed to scale up the number of instances while maintaining the scope of the instance relation class. Extensive experiments on FewRel and FewRel2.0 public datasets are conducted and evaluate the effectiveness of BMAN.

show abstract

Section: Methodsmentioning

confidence: 99%

Bidirectional matching and aggregation network for few-shot relation extraction

Wei

Guo

Zhang

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Accuracy is adopted as the evaluation metric Accuracy = TP + TN TP + FP + FN + TN (15) where TP + TN is the number of queries correctly classified, and TP + FP + FN + TN are the number of all queries.…”

Section: Methodsmentioning

confidence: 99%

“…The model aimed to classify query instances, and sought basic knowledge about supporting examples to obtain a better example representation. Wang et al [15] proposed the CTEG model, which is trained by entity-guided attention and is confusion-aware to decouple easily confused relations. Optimization-based methods follow the idea of differentiating an optimization process over support-set within the meta-learning framework.…”

Section: Few-shot Relation Classificationmentioning

confidence: 99%

Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification

Jin

Zhang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Compared with the traditional few-shot task, the few-shot none-of-the-above (NOTA) relation classification focuses on the realistic scenario of few-shot learning, in which a test instance might not belong to any of the target categories. This undoubtedly increases the task’s difficulty because given only a few support samples, this cannot represent the distribution of NOTA categories in space. The model needs to make full use of the syntactic information and word meaning information learned in the pre-training stage to distinguish the NOTA category and the support sample category in the embedding space. However, previous fine-tuning methods mainly focus on optimizing the extra classifiers (on top of pre-trained language models (PLMs)) and neglect the connection between pre-training objectives and downstream tasks. In this paper, we propose the commonsense knowledge-aware prompt tuning (CKPT) method for a few-shot NOTA relation classification task. First, a simple and effective prompt-learning method is developed by constructing relation-oriented templates, which can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks. Second, external knowledge is incorporated into the model by a label-extension operation, which forms knowledgeable prompt tuning to improve and stabilize prompt tuning. Third, to distinguish the NOTA pairs and positive pairs in embedding space more accurately, a learned scoring strategy is proposed, which introduces a learned threshold classification function and improves the loss function by adding a new term focused on NOTA identification. Experiments on two widely used benchmarks (FewRel 2.0 and Few-shot TACRED) show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field.

show abstract

“…In order to alleviate the problem of insufficient training data, MICK (Geng et al, 2020) learns general language rules and grammatical knowledge from cross-domain datasets. Wang (Wang et al, 2020) proposes the CTEG model to solve the relation confusion problem of FSRE. Cong (Cong et al, 2020) proposes an inductive clustering based framework, DaFeC, to solve the problem of domain adaptation in FSRE.…”

Section: Introductionmentioning

confidence: 99%

Entity Concept-enhanced Few-shot Relation Extraction

Yang¹,

Zhang²,

Niu³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data.Most existing FSRE algorithms fail to accurately classify the relations merely based on the information of the sentences together with the recognized entity pairs, due to limited samples and lack of knowledge. To address this problem, in this paper, we proposed a novel entity CONCEPTenhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction and boost the relations classification performance. Firstly, a conceptsentence attention module is developed to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts. Secondly, a self-attention based fusion module is presented to bridge the gap of concept embedding and sentence embedding from different semantic spaces. Extensive experiments on the FSRE benchmark dataset FewRel have demonstrated the effectiveness and the superiority of the proposed ConceptFERE scheme as compared to the state-of-the-art baselines. Code is available at https://github.com/LittleGuoKe/ConceptFERE.

show abstract

Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training

Cited by 32 publications

References 24 publications

Bidirectional matching and aggregation network for few-shot relation extraction

Bidirectional matching and aggregation network for few-shot relation extraction

Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification

Entity Concept-enhanced Few-shot Relation Extraction

Contact Info

Product

Resources

About