SWEET - Weakly Supervised Person Name Extraction for Fighting Human Trafficking

Javin Liu, Hao Yu, Vidya Sujaya, Pratheeksha Nair, Kellin Pelrine, Reihaneh Rabbany

Abstract
In this work, we propose a weak supervision pipeline SWEET: Supervise Weakly for Entity Extraction to fight Trafficking for extracting person names from noisy escort advertisements. Our method combines the simplicity of rule-matching (through antirules, i.e., negated rules) and the generalizability of large language models fine-tuned on benchmark, domain-specific and synthetic datasets, treating them as weak labels. One of the major challenges in this domain is limited labeled data. SWEET addresses this by obtaining multiple weak labels through labeling functions and effectively aggregating them. SWEET outperforms the previous supervised SOTA method for this task by 9% F1 score on domain data and better generalizes to common benchmark datasets. Furthermore, we also release HTGEN, a synthetically generated dataset of escort advertisements (built using ChatGPT) to facilitate further research within the community.
Anthology ID:
2023.findings-emnlp.219
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3355–3367
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.219
DOI:
10.18653/v1/2023.findings-emnlp.219
Bibkey:
Cite (ACL):
Javin Liu, Hao Yu, Vidya Sujaya, Pratheeksha Nair, Kellin Pelrine, and Reihaneh Rabbany. 2023. SWEET - Weakly Supervised Person Name Extraction for Fighting Human Trafficking. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3355–3367, Singapore. Association for Computational Linguistics.
Cite (Informal):
SWEET - Weakly Supervised Person Name Extraction for Fighting Human Trafficking (Liu et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.findings-emnlp.219.pdf
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