Adversarial Domain Adaptation for Duplicate Question Detection

TL;DR

This paper explores adversarial domain adaptation to improve duplicate question detection in forums lacking annotated data, demonstrating significant performance gains across multiple domain pairs.

Contribution

It introduces an adversarial domain adaptation approach tailored for duplicate question detection and analyzes factors influencing its effectiveness.

Findings

Average 5.6% improvement over baselines

Effectiveness depends on domain similarity and data properties

Provides insights into when adversarial adaptation works best

Abstract

We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.