Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports
Abstract
Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. However, training or fine-tuning these models for individual tasks can be time consuming and resource intensive. Thus, a lot of current research is focused on using transformers for multi-task learning (Raffel et al.,2020) and how to group the tasks to help a multi-task model to learn effective representations that can be shared across tasks (Standley et al., 2020; Fifty et al., 2021). In this work, we show that a single multi-tasking model can match the performance of task specific models when the task specific models show similar representations across all of their hidden layers and their gradients are aligned, i.e. their gradients follow the same direction. We hypothesize that the above observations explain the effectiveness of multi-task learning. We validate our observations on our internal radiologist-annotated datasets on the cervical and lumbar spine. Our method is simple and intuitive, and can be used in a wide range of NLP problems.
Cite
@article{arxiv.2205.02979,
title = {Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports},
author = {Arijit Sehanobish and McCullen Sandora and Nabila Abraham and Jayashri Pawar and Danielle Torres and Anasuya Das and Murray Becker and Richard Herzog and Benjamin Odry and Ron Vianu},
journal= {arXiv preprint arXiv:2205.02979},
year = {2022}
}
Comments
To appear at NAACL-2022, Industry Track. Follow-up of previous work: arXiv:2204.04544