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        <title>Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification</title>
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        <namePart type="given">Zhi-Xiu</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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    <abstract>This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of the support set for each relation candidate independently. On the contrary, our proposed MLMAN model encodes the query instance and each support set in an interactive way by considering their matching information at both local and instance levels. The final class prototype for each support set is obtained by attentive aggregation over the representations of support instances, where the weights are calculated using the query instance. Experimental results demonstrate the effectiveness of our proposed methods, which achieve a new state-of-the-art performance on the FewRel dataset.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1277</identifier>
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%0 Conference Proceedings
%T Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
%A Ye, Zhi-Xiu
%A Ling, Zhen-Hua
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F ye-ling-2019-multi
%X This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of the support set for each relation candidate independently. On the contrary, our proposed MLMAN model encodes the query instance and each support set in an interactive way by considering their matching information at both local and instance levels. The final class prototype for each support set is obtained by attentive aggregation over the representations of support instances, where the weights are calculated using the query instance. Experimental results demonstrate the effectiveness of our proposed methods, which achieve a new state-of-the-art performance on the FewRel dataset.
%R 10.18653/v1/P19-1277
%U https://aclanthology.org/P19-1277/
%U https://doi.org/10.18653/v1/P19-1277
%P 2872-2881

Markdown (Informal)

[Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification](https://aclanthology.org/P19-1277/) (Ye & Ling, ACL 2019)

• Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification (Ye & Ling, ACL 2019)

ACL

• Zhi-Xiu Ye and Zhen-Hua Ling. 2019. Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2872–2881, Florence, Italy. Association for Computational Linguistics.