@inproceedings{ea21238694474654a3376a5c8608fc82,
title = "Ad-hoc document retrieval using weak-supervision with BERT and GPT2",
abstract = "We describe a weakly-supervised method for training deep learning models for the task of ad-hoc document retrieval. Our method is based on generative and discriminative models that are trained using weak-supervision based solely on the documents in the corpus. We present an end-to-end retrieval system that starts with traditional information retrieval methods, followed by two deep learning re-rankers. We evaluate our method on three different datasets: a COVID-19 related scientific literature dataset and two news datasets. We show that our method outperforms state-of-the-art methods; this without the need for the expensive process of manually labeling data.",
author = "Yosi Mass and Haggai Roitman",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics; 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 ; Conference date: 16-11-2020 Through 20-11-2020",
year = "2020",
month = jan,
day = "1",
doi = "10.18653/v1/2020.emnlp-main.343",
language = "English",
series = "EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "4191--4197",
booktitle = "EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
address = "United States",
}