A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering
This addresses the need for better QA on specialized documents, though it is incremental as it builds on existing synthetic data methods.
The paper tackles the problem of question answering (QA) models struggling with out-of-domain documents by generating synthetic domain-specific data, resulting in an 8.75 F1 improvement in QA performance.
Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on out-of-domain documents without fine-tuning. We demonstrate that synthetic domain-specific datasets can be generated easily using domain-general models, while still providing significant improvements to QA performance. We present two new tools for this task: A flexible pipeline for validating the synthetic QA data and training downstream models on it, and an online interface to facilitate human annotation of this generated data. Using this interface, crowdworkers labelled 1117 synthetic QA pairs, which we then used to fine-tune downstream models and improve domain-specific QA performance by 8.75 F1.