@inproceedings{tong-etal-2017-combating,
title = "Combating Human Trafficking with Multimodal Deep Models",
author = "Tong, Edmund and
Zadeh, Amir and
Jones, Cara and
Morency, Louis-Philippe",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1142",
doi = "10.18653/v1/P17-1142",
pages = "1547--1556",
abstract = "Human trafficking is a global epidemic affecting millions of people across the planet. Sex trafficking, the dominant form of human trafficking, has seen a significant rise mostly due to the abundance of escort websites, where human traffickers can openly advertise among at-will escort advertisements. In this paper, we take a major step in the automatic detection of advertisements suspected to pertain to human trafficking. We present a novel dataset called Trafficking-10k, with more than 10,000 advertisements annotated for this task. The dataset contains two sources of information per advertisement: text and images. For the accurate detection of trafficking advertisements, we designed and trained a deep multimodal model called the Human Trafficking Deep Network (HTDN).",
}
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%0 Conference Proceedings
%T Combating Human Trafficking with Multimodal Deep Models
%A Tong, Edmund
%A Zadeh, Amir
%A Jones, Cara
%A Morency, Louis-Philippe
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F tong-etal-2017-combating
%X Human trafficking is a global epidemic affecting millions of people across the planet. Sex trafficking, the dominant form of human trafficking, has seen a significant rise mostly due to the abundance of escort websites, where human traffickers can openly advertise among at-will escort advertisements. In this paper, we take a major step in the automatic detection of advertisements suspected to pertain to human trafficking. We present a novel dataset called Trafficking-10k, with more than 10,000 advertisements annotated for this task. The dataset contains two sources of information per advertisement: text and images. For the accurate detection of trafficking advertisements, we designed and trained a deep multimodal model called the Human Trafficking Deep Network (HTDN).
%R 10.18653/v1/P17-1142
%U https://aclanthology.org/P17-1142
%U https://doi.org/10.18653/v1/P17-1142
%P 1547-1556
Markdown (Informal)
[Combating Human Trafficking with Multimodal Deep Models](https://aclanthology.org/P17-1142) (Tong et al., ACL 2017)
ACL
- Edmund Tong, Amir Zadeh, Cara Jones, and Louis-Philippe Morency. 2017. Combating Human Trafficking with Multimodal Deep Models. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1547–1556, Vancouver, Canada. Association for Computational Linguistics.