A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering

TL;DR

This paper introduces a pipeline and tools for generating and annotating synthetic domain-specific QA data, significantly improving question answering performance on out-of-domain documents.

Contribution

It presents a novel pipeline and online annotation interface for creating synthetic QA datasets, enhancing domain-specific QA models without extensive real data.

Findings

Synthetic data improved QA F1 by 8.75 points.

Crowdworkers labeled 1117 synthetic QA pairs.

Tools facilitate data validation and human annotation.

Abstract

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.