On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method

TL;DR

This paper conducts a large-scale robustness study of dialogue history modeling in Conversational Question Answering, revealing that high benchmark scores do not ensure robustness, and introduces a simple prompt-based method that improves robustness across various settings.

Contribution

It provides the first comprehensive robustness analysis of history modeling in CQA and proposes a novel prompt-based approach that enhances robustness.

Findings

Benchmark scores do not guarantee robustness.

Different methods perform variably under different settings.

The proposed prompt-based method shows strong robustness.

Abstract

Most works on modeling the conversation history in Conversational Question Answering (CQA) report a single main result on a common CQA benchmark. While existing models show impressive results on CQA leaderboards, it remains unclear whether they are robust to shifts in setting (sometimes to more realistic ones), training data size (e.g. from large to small sets) and domain. In this work, we design and conduct the first large-scale robustness study of history modeling approaches for CQA. We find that high benchmark scores do not necessarily translate to strong robustness, and that various methods can perform extremely differently under different settings. Equipped with the insights from our study, we design a novel prompt-based history modeling approach, and demonstrate its strong robustness across various settings. Our approach is inspired by existing methods that highlight historic answers in the passage. However, instead of highlighting by modifying the passage token embeddings, we add textual prompts directly in the passage text. Our approach is simple, easy-to-plug into practically any model, and highly effective, thus we recommend it as a starting point for future model developers. We also hope that our study and insights will raise awareness to the importance of robustness-focused evaluation, in addition to obtaining high leaderboard scores, leading to better CQA systems.