Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)

• URL: http://arxiv.org/abs/2205.01878v1
• Date: Wed, 4 May 2022 03:54:44 GMT
• Title: Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
• Authors: YI Liang and Shuai Zhao and Bo Cheng and Yuwei Yin and Hao Yang
• Abstract summary: Few-shot relation learning refers to infer facts for relations with a limited number of observed triples. In this paper, we explore this kind of fine-grained semantic meanings and propose our model TransAM. Experiments on two public benchmark datasets NELL-One and Wiki-One with 1-shot setting prove the effectiveness of TransAM.
• Score: 26.487611287367443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot relation learning refers to infer facts for relations with a limited number of observed triples. Existing metric-learning methods for this problem mostly neglect entity interactions within and between triples. In this paper, we explore this kind of fine-grained semantic meanings and propose our model TransAM. Specifically, we serialize reference entities and query entities into sequence and apply transformer structure with local-global attention to capture both intra- and inter-triple entity interactions. Experiments on two public benchmark datasets NELL-One and Wiki-One with 1-shot setting prove the effectiveness of TransAM.

Related papers

• All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction [33.09413959289308]
  We propose a flexible framework dubbed heterogeneous interaction rating network (HIRE) HIRE dose not solely rely on the pre-defined interaction pattern or the manually constructed heterogeneous information network. In the experiments, we evaluate our model under three cold-start settings on three real-world datasets.
  arXiv  Detail & Related papers  (2024-03-26T14:29:34Z)
• CARE: Co-Attention Network for Joint Entity and Relation Extraction [0.0]
  We propose a Co-Attention network for joint entity and relation extraction. Our approach includes adopting a parallel encoding strategy to learn separate representations for each subtask. At the core of our approach is the co-attention module that captures two-way interaction between the two subtasks.
  arXiv  Detail & Related papers  (2023-08-24T03:40:54Z)
• Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
  Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph. We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
  arXiv  Detail & Related papers  (2023-07-19T02:11:19Z)
• From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment [17.70562397382911]
  Existing methods usually encode the triples of entities as embeddings and learn to align the embeddings. We transform both triples into unified textual sequences, and model the EA task as a bi-directional textual entailment task. Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information.
  arXiv  Detail & Related papers  (2023-05-19T08:06:50Z)
• UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction [29.15806644012706]
  We propose UniRel to address the challenges of capturing rich correlations between entities and relations. Specifically, we unify representations of entities and relations by jointly encoding them within a relationald natural language sequence. With comprehensive experiments on two popular triple extraction datasets, we demonstrate that UniRel is more effective computationally efficient.
  arXiv  Detail & Related papers  (2022-11-16T16:53:13Z)
• Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning [18.374097786748834]
  Document-level Relation Extraction (DRE) aims to recognize the relations between two entities. Few previous studies have investigated the mention integration, which may be problematic. We propose two novel techniques, Context Guided Mention Integration and Inter-pair Reasoning.
  arXiv  Detail & Related papers  (2022-01-13T08:00:23Z)
• Link Prediction on N-ary Relational Data Based on Relatedness Evaluation [61.61555159755858]
  We propose a method called NaLP to conduct link prediction on n-ary relational data. We represent each n-ary relational fact as a set of its role and role-value pairs. Experimental results validate the effectiveness and merits of the proposed methods.
  arXiv  Detail & Related papers  (2021-04-21T09:06:54Z)
• Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training [49.9995628166064]
  We propose CTEG, a model equipped with two mechanisms to learn to decouple easily-confused relations. On the one hand, an EGA mechanism is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations.
  arXiv  Detail & Related papers  (2020-10-21T11:07:53Z)
• Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks [61.950353376870154]
  Joint-event-extraction is a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. We propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of triggers or entities. Our approach outperforms the state-of-the-art methods in both entity and trigger extraction.
  arXiv  Detail & Related papers  (2020-10-13T11:51:17Z)
• DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification [77.59549450705384]
  In dialog system, dialog act recognition and sentiment classification are two correlative tasks. Most of the existing systems either treat them as separate tasks or just jointly model the two tasks. We propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitly consider the cross-impact and model the interaction between the two tasks.
  arXiv  Detail & Related papers  (2020-08-16T14:13:32Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

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.