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        <title>Adaptive Attentional Network for Few-Shot Knowledge Graph Completion</title>
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        <namePart type="given">Lihong</namePart>
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    <abstract>Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions. Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations. This will be more predictive for knowledge acquisition in the few-shot scenario. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes. The source code is available at https://github.com/JiaweiSheng/FAAN.</abstract>
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%0 Conference Proceedings
%T Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
%A Sheng, Jiawei
%A Guo, Shu
%A Chen, Zhenyu
%A Yue, Juwei
%A Wang, Lihong
%A Liu, Tingwen
%A Xu, Hongbo
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sheng-etal-2020-adaptive
%X Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions. Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations. This will be more predictive for knowledge acquisition in the few-shot scenario. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes. The source code is available at https://github.com/JiaweiSheng/FAAN.
%R 10.18653/v1/2020.emnlp-main.131
%U https://aclanthology.org/2020.emnlp-main.131/
%U https://doi.org/10.18653/v1/2020.emnlp-main.131
%P 1681-1691

Markdown (Informal)

[Adaptive Attentional Network for Few-Shot Knowledge Graph Completion](https://aclanthology.org/2020.emnlp-main.131/) (Sheng et al., EMNLP 2020)

• Adaptive Attentional Network for Few-Shot Knowledge Graph Completion (Sheng et al., EMNLP 2020)

ACL

• Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, and Hongbo Xu. 2020. Adaptive Attentional Network for Few-Shot Knowledge Graph Completion. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1681–1691, Online. Association for Computational Linguistics.