# Task Transfer and Domain Adaptation for Zero-Shot Question Answering Source: [Task Transfer and Domain Adaptation for Zero-Shot Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:18:38.162Z Authors: Xiang Pan, Alex Sheng, David Shimshoni, Aditya Singhal, Sara Rosenthal, Avirup Sil Published: 2022-06-14T09:10:48.000Z ## Abstract Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks. However, when applying machine learning methods to new domains, labeled data may not always be available. To address this, we use supervised pretraining on source-domain data to reduce sample complexity on domain-specific downstream tasks. We evaluate zero-shot performance on domain-specific reading comprehension tasks by combining task transfer with domain adaptation to fine-tune a pretrained model with no labelled data from the target task. Our approach outperforms Domain-Adaptive Pretraining on downstream domain-specific reading comprehension tasks in 3 out of 4 domains. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()