@inproceedings{chen-etal-2021-hitter,
title = "{H}itt{ER}: Hierarchical Transformers for Knowledge Graph Embeddings",
author = "Chen, Sanxing and
Liu, Xiaodong and
Gao, Jianfeng and
Jiao, Jian and
Zhang, Ruofei and
Ji, Yangfeng",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.812",
doi = "10.18653/v1/2021.emnlp-main.812",
pages = "10395--10407",
abstract = "This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity{'}s neighborhood. Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block. We further design a masked entity prediction task to balance information from the relational context and the source entity itself. Experimental results show that HittER achieves new state-of-the-art results on multiple link prediction datasets. We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets.",
}
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<abstract>This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity’s neighborhood. Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block. We further design a masked entity prediction task to balance information from the relational context and the source entity itself. Experimental results show that HittER achieves new state-of-the-art results on multiple link prediction datasets. We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets.</abstract>
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%0 Conference Proceedings
%T HittER: Hierarchical Transformers for Knowledge Graph Embeddings
%A Chen, Sanxing
%A Liu, Xiaodong
%A Gao, Jianfeng
%A Jiao, Jian
%A Zhang, Ruofei
%A Ji, Yangfeng
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F chen-etal-2021-hitter
%X This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity’s neighborhood. Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block. We further design a masked entity prediction task to balance information from the relational context and the source entity itself. Experimental results show that HittER achieves new state-of-the-art results on multiple link prediction datasets. We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets.
%R 10.18653/v1/2021.emnlp-main.812
%U https://aclanthology.org/2021.emnlp-main.812
%U https://doi.org/10.18653/v1/2021.emnlp-main.812
%P 10395-10407
Markdown (Informal)
[HittER: Hierarchical Transformers for Knowledge Graph Embeddings](https://aclanthology.org/2021.emnlp-main.812) (Chen et al., EMNLP 2021)
ACL
- Sanxing Chen, Xiaodong Liu, Jianfeng Gao, Jian Jiao, Ruofei Zhang, and Yangfeng Ji. 2021. HittER: Hierarchical Transformers for Knowledge Graph Embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10395–10407, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.