Ad-hoc Document Retrieval using Weak-Supervision with BERT and GPT2
Yosi Mass, Haggai Roitman · 2020
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-ofthe-art methods; this without the need for the expensive process of manually labeling data.