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        <title>Hyperbolic Capsule Networks for Multi-Label Classification</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>Although deep neural networks are effective at extracting high-level features, classification methods usually encode an input into a vector representation via simple feature aggregation operations (e.g. pooling). Such operations limit the performance. For instance, a multi-label document may contain several concepts. In this case, one vector can not sufficiently capture its salient and discriminative content. Thus, we propose Hyperbolic Capsule Networks (HyperCaps) for Multi-Label Classification (MLC), which have two merits. First, hyperbolic capsules are designed to capture fine-grained document information for each label, which has the ability to characterize complicated structures among labels and documents. Second, Hyperbolic Dynamic Routing (HDR) is introduced to aggregate hyperbolic capsules in a label-aware manner, so that the label-level discriminative information can be preserved along the depth of neural networks. To efficiently handle large-scale MLC datasets, we additionally present a new routing method to adaptively adjust the capsule number during routing. Extensive experiments are conducted on four benchmark datasets. Compared with the state-of-the-art methods, HyperCaps significantly improves the performance of MLC especially on tail labels.</abstract>
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%0 Conference Proceedings
%T Hyperbolic Capsule Networks for Multi-Label Classification
%A Chen, Boli
%A Huang, Xin
%A Xiao, Lin
%A Jing, Liping
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-hyperbolic
%X Although deep neural networks are effective at extracting high-level features, classification methods usually encode an input into a vector representation via simple feature aggregation operations (e.g. pooling). Such operations limit the performance. For instance, a multi-label document may contain several concepts. In this case, one vector can not sufficiently capture its salient and discriminative content. Thus, we propose Hyperbolic Capsule Networks (HyperCaps) for Multi-Label Classification (MLC), which have two merits. First, hyperbolic capsules are designed to capture fine-grained document information for each label, which has the ability to characterize complicated structures among labels and documents. Second, Hyperbolic Dynamic Routing (HDR) is introduced to aggregate hyperbolic capsules in a label-aware manner, so that the label-level discriminative information can be preserved along the depth of neural networks. To efficiently handle large-scale MLC datasets, we additionally present a new routing method to adaptively adjust the capsule number during routing. Extensive experiments are conducted on four benchmark datasets. Compared with the state-of-the-art methods, HyperCaps significantly improves the performance of MLC especially on tail labels.
%R 10.18653/v1/2020.acl-main.283
%U https://aclanthology.org/2020.acl-main.283
%U https://doi.org/10.18653/v1/2020.acl-main.283
%P 3115-3124

Markdown (Informal)

[Hyperbolic Capsule Networks for Multi-Label Classification](https://aclanthology.org/2020.acl-main.283) (Chen et al., ACL 2020)

• Hyperbolic Capsule Networks for Multi-Label Classification (Chen et al., ACL 2020)

ACL

• Boli Chen, Xin Huang, Lin Xiao, and Liping Jing. 2020. Hyperbolic Capsule Networks for Multi-Label Classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3115–3124, Online. Association for Computational Linguistics.