One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning

TL;DR

This paper introduces a comprehensive supervised contrastive learning approach to improve QA systems by enhancing robustness, efficiency, and functionality across multiple tasks with minimal tuning.

Contribution

It proposes a unified SCL-based method to address key QA tasks, enabling better intent classification, detection, discovery, and continual learning.

Findings

Achieves state-of-the-art performance on multiple QA tasks.

Improves model efficiency with minimal additional tuning.

Effectively detects and discovers new user intents.

Abstract

This paper presents a novel and comprehensive solution to enhance both the robustness and efficiency of question answering (QA) systems through supervised contrastive learning (SCL). Training a high-performance QA system has become straightforward with pre-trained language models, requiring only a small amount of data and simple fine-tuning. However, despite recent advances, existing QA systems still exhibit significant deficiencies in functionality and training efficiency. We address the functionality issue by defining four key tasks: user input intent classification, out-of-domain input detection, new intent discovery, and continual learning. We then leverage a unified SCL-based representation learning method to efficiently build an intra-class compact and inter-class scattered feature space, facilitating both known intent classification and unknown intent detection and discovery. Consequently, with minimal additional tuning on downstream tasks, our approach significantly improves model efficiency and achieves new state-of-the-art performance across all tasks.