@inproceedings{bose-etal-2018-adversarial,
title = "Adversarial Contrastive Estimation",
author = "Bose, Avishek Joey and
Ling, Huan and
Cao, Yanshuai",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1094",
doi = "10.18653/v1/P18-1094",
pages = "1021--1032",
abstract = "Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.",
}
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%0 Conference Proceedings
%T Adversarial Contrastive Estimation
%A Bose, Avishek Joey
%A Ling, Huan
%A Cao, Yanshuai
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 jul
%I Association for Computational Linguistics
%C Melbourne, Australia
%F bose-etal-2018-adversarial
%X Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.
%R 10.18653/v1/P18-1094
%U https://aclanthology.org/P18-1094
%U https://doi.org/10.18653/v1/P18-1094
%P 1021-1032
Markdown (Informal)
[Adversarial Contrastive Estimation](https://aclanthology.org/P18-1094) (Bose et al., ACL 2018)
ACL
- Avishek Joey Bose, Huan Ling, and Yanshuai Cao. 2018. Adversarial Contrastive Estimation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1021–1032, Melbourne, Australia. Association for Computational Linguistics.