Supervised Graph Contrastive Pretraining for Text Classification

TL;DR

This paper introduces a supervised graph contrastive pretraining method for text classification that leverages labeled data from related tasks, improving generalization and performance across multiple datasets and settings.

Contribution

The paper proposes a novel graph-based supervised contrastive learning approach that incorporates label information from related tasks into token embeddings for text classification.

Findings

Outperforms existing pretraining schemes by 2.5% on average.

Achieves 1.8% improvement over example-level contrastive learning.

Demonstrates 3.91% average gain in zero-shot cross-domain tasks.

Abstract

Contrastive pretraining techniques for text classification has been largely studied in an unsupervised setting. However, oftentimes labeled data from related tasks which share label semantics with current task is available. We hypothesize that using this labeled data effectively can lead to better generalization on current task. In this paper, we propose a novel way to effectively utilize labeled data from related tasks with a graph based supervised contrastive learning approach. We formulate a token-graph by extrapolating the supervised information from examples to tokens. Our formulation results in an embedding space where tokens with high/low probability of belonging to same class are near/further-away from one another. We also develop detailed theoretical insights which serve as a motivation for our method. In our experiments with $13$ datasets, we show our method outperforms pretraining schemes by $2.5\%$ and also example-level contrastive learning based formulation by $1.8\%$ on average. In addition, we show cross-domain effectiveness of our method in a zero-shot setting by $3.91\%$ on average. Lastly, we also demonstrate our method can be used as a noisy teacher in a knowledge distillation setting to significantly improve performance of transformer based models in low labeled data regime by $4.57\%$ on average.