Contrastive-Learning-Enhanced Semantic Representation Learning for Text Prediction

Abstract

The rapid evolution of natural language processing has brought text prediction to the forefront of artificial intelligence research, driving applications ranging from intelligent writing assistants to advanced dialogue systems. Despite the remarkable success of autoregressive language models and transformer-based architectures, these systems frequently suffer from representation degeneration, where the learned semantic embeddings occupy a narrow cone in the latent space. This phenomenon severely limits the model capacity to distinguish between subtly different semantic contexts, leading to generic, repetitive, or contextually misaligned predictions. To address this critical limitation, we propose a novel framework that integrates contrastive learning into the semantic representation learning pipeline for text prediction. By explicitly optimizing the latent space uniformity and alignment through a contrastive objective, our methodology forces the model to pull semantically equivalent representations closer while pushing apart dissimilar ones. We introduce a joint optimization strategy that seamlessly combines the standard autoregressive cross-entropy loss with an InfoNCE-based contrastive loss, employing advanced data augmentation techniques tailored for textual data, such as adversarial dropout and contextual synonym replacement. Extensive empirical evaluations conducted on large-scale corpora demonstrate that our approach significantly reduces perplexity and enhances generation diversity compared to state-of-the-art baselines. Furthermore, ablation studies confirm the efficacy of our augmentation strategies and the robustness of the contrastive objective across various hyperparameter settings. This research provides a robust theoretical foundation and a highly effective practical methodology for advancing the next generation of text prediction systems.