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        <title>Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction</title>
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Bi-encoder architectures for distantly-supervised relation extraction are designed to make use of the complementary information found in text and knowledge graphs (KG).However, current architectures suffer from two drawbacks. They either do not allow any sharing between the text encoder and the KG encoder at all, or, in case of models with KG-to-text attention, only share information in one direction. Here, we introduce cross-stitch bi-encoders, which allow full interaction between the text encoder and the KG encoder via a cross-stitch mechanism. The cross-stitch mechanism allows sharing and updating representations between the two encoders at any layer, with the amount of sharing being dynamically controlled via cross-attention-based gates. Experimental results on two relation extraction benchmarks from two different domains show that enabling full interaction between the two encoders yields strong improvements.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.467</identifier>
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%0 Conference Proceedings
%T Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction
%A Dai, Qin
%A Heinzerling, Benjamin
%A Inui, Kentaro
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F dai-etal-2022-cross
%X Bi-encoder architectures for distantly-supervised relation extraction are designed to make use of the complementary information found in text and knowledge graphs (KG).However, current architectures suffer from two drawbacks. They either do not allow any sharing between the text encoder and the KG encoder at all, or, in case of models with KG-to-text attention, only share information in one direction. Here, we introduce cross-stitch bi-encoders, which allow full interaction between the text encoder and the KG encoder via a cross-stitch mechanism. The cross-stitch mechanism allows sharing and updating representations between the two encoders at any layer, with the amount of sharing being dynamically controlled via cross-attention-based gates. Experimental results on two relation extraction benchmarks from two different domains show that enabling full interaction between the two encoders yields strong improvements.
%R 10.18653/v1/2022.emnlp-main.467
%U https://aclanthology.org/2022.emnlp-main.467
%U https://doi.org/10.18653/v1/2022.emnlp-main.467
%P 6947-6958

Markdown (Informal)

[Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction](https://aclanthology.org/2022.emnlp-main.467) (Dai et al., EMNLP 2022)

• Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction (Dai et al., EMNLP 2022)

ACL

• Qin Dai, Benjamin Heinzerling, and Kentaro Inui. 2022. Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6947–6958, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.