@inproceedings{chen-etal-2018-enhancing,
title = "Enhancing Sentence Embedding with Generalized Pooling",
author = "Chen, Qian and
Ling, Zhen-Hua and
Zhu, Xiaodan",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1154",
pages = "1815--1826",
abstract = "Pooling is an essential component of a wide variety of sentence representation and embedding models. This paper explores generalized pooling methods to enhance sentence embedding. We propose vector-based multi-head attention that includes the widely used max pooling, mean pooling, and scalar self-attention as special cases. The model benefits from properly designed penalization terms to reduce redundancy in multi-head attention. We evaluate the proposed model on three different tasks: natural language inference (NLI), author profiling, and sentiment classification. The experiments show that the proposed model achieves significant improvement over strong sentence-encoding-based methods, resulting in state-of-the-art performances on four datasets. The proposed approach can be easily implemented for more problems than we discuss in this paper.",
}
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%0 Conference Proceedings
%T Enhancing Sentence Embedding with Generalized Pooling
%A Chen, Qian
%A Ling, Zhen-Hua
%A Zhu, Xiaodan
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F chen-etal-2018-enhancing
%X Pooling is an essential component of a wide variety of sentence representation and embedding models. This paper explores generalized pooling methods to enhance sentence embedding. We propose vector-based multi-head attention that includes the widely used max pooling, mean pooling, and scalar self-attention as special cases. The model benefits from properly designed penalization terms to reduce redundancy in multi-head attention. We evaluate the proposed model on three different tasks: natural language inference (NLI), author profiling, and sentiment classification. The experiments show that the proposed model achieves significant improvement over strong sentence-encoding-based methods, resulting in state-of-the-art performances on four datasets. The proposed approach can be easily implemented for more problems than we discuss in this paper.
%U https://aclanthology.org/C18-1154
%P 1815-1826
Markdown (Informal)
[Enhancing Sentence Embedding with Generalized Pooling](https://aclanthology.org/C18-1154) (Chen et al., COLING 2018)
ACL
- Qian Chen, Zhen-Hua Ling, and Xiaodan Zhu. 2018. Enhancing Sentence Embedding with Generalized Pooling. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1815–1826, Santa Fe, New Mexico, USA. Association for Computational Linguistics.