Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering

TL;DR

This paper systematically compares extractive and generative question answering models using nine transformer-based PrLMs, revealing their relative strengths in different contexts and guiding future research directions.

Contribution

First comprehensive study comparing extractive and generative QA readers across multiple transformer architectures, providing insights into their strengths and weaknesses.

Findings

Generative readers excel in long context QA.

Extractive readers perform better in short context and out-of-domain settings.

Encoder of encoder-decoder PrLMs like T5 is a strong extractive reader.

Abstract

While both extractive and generative readers have been successfully applied to the Question Answering (QA) task, little attention has been paid toward the systematic comparison of them. Characterizing the strengths and weaknesses of the two readers is crucial not only for making a more informed reader selection in practice but also for developing a deeper understanding to foster further research on improving readers in a principled manner. Motivated by this goal, we make the first attempt to systematically study the comparison of extractive and generative readers for question answering. To be aligned with the state-of-the-art, we explore nine transformer-based large pre-trained language models (PrLMs) as backbone architectures. Furthermore, we organize our findings under two main categories: (1) keeping the architecture invariant, and (2) varying the underlying PrLMs. Among several interesting findings, it is important to highlight that (1) the generative readers perform better in long context QA, (2) the extractive readers perform better in short context while also showing better out-of-domain generalization, and (3) the encoder of encoder-decoder PrLMs (e.g., T5) turns out to be a strong extractive reader and outperforms the standard choice of encoder-only PrLMs (e.g., RoBERTa). We also study the effect of multi-task learning on the two types of readers varying the underlying PrLMs and perform qualitative and quantitative diagnosis to provide further insights into future directions in modeling better readers.