CERT: Contrastive Self-supervised Learning for Language Understanding (2005.12766v2)

Abstract: Pretrained LLMs such as BERT, GPT have shown great effectiveness in language understanding. The auxiliary predictive tasks in existing pretraining approaches are mostly defined on tokens, thus may not be able to capture sentence-level semantics very well. To address this issue, we propose CERT: Contrastive self-supervised Encoder Representations from Transformers, which pretrains language representation models using contrastive self-supervised learning at the sentence level. CERT creates augmentations of original sentences using back-translation. Then it finetunes a pretrained language encoder (e.g., BERT) by predicting whether two augmented sentences originate from the same sentence. CERT is simple to use and can be flexibly plugged into any pretraining-finetuning NLP pipeline. We evaluate CERT on 11 natural language understanding tasks in the GLUE benchmark where CERT outperforms BERT on 7 tasks, achieves the same performance as BERT on 2 tasks, and performs worse than BERT on 2 tasks. On the averaged score of the 11 tasks, CERT outperforms BERT. The data and code are available at https://github.com/UCSD-AI4H/CERT

Analysis of CERT: Contrastive Self-supervised Learning for Language Understanding

The paper "CERT: Contrastive Self-supervised Learning for Language Understanding" introduces CERT (Contrastive self-supervised Encoder Representations from Transformers), a method aimed at enhancing sentence-level semantics in pretrained LLMs. This is achieved through the integration of contrastive self-supervised learning (CSSL). While existing pretrained models, such as BERT and GPT, have demonstrated considerable effectiveness in various NLP tasks, they primarily focus on token-level predictions, potentially overlooking global sentence-level semantics.

Methodological Overview

CERT leverages contrastive self-supervised learning, inspired by methodologies effective in image representation learning frameworks such as MoCo. CERT employs back-translation to generate sentence-level augmentations and reinforces learning by predicting whether pairs of augmented sentences are derived from the same original sentence. This introduces a sentence-centric approach contrasting strongly with traditional token-level pretraining tasks.

1. Back-Translation Augmentation: Sentences are translated to another language and back, providing variant constructs of the same sentence without altering the fundamental meaning, thus maintaining sentence-level information intact.
2. Momentum Contrast Mechanism: CERT uses a queue-based version of CSSL, akin to MoCo, enabling the decoupling of batch size from the number of negative samples, which is beneficial for computational efficiency and storage.
3. Contrastive Objective: The model learns to distinguish between positive pairs (augmented from the same sentence) and negative pairs (stemming from different sentences) using a contrastive loss. The embeddings are refined by minimizing this loss, enhancing their ability to capture sentence-level semantics.

Experimental Results

The experimental evaluation of CERT was conducted across 11 tasks on the GLUE benchmark. The results were indicative of CERT's efficacy:

• CERT surpassed BERT on 7 tasks, illustrated no inferior performance on 2 tasks, and performed similarly to BERT on the remaining tasks when considering the average scores. This consistent performance improvement indicates CERT's potential in advancing sentence-level semantic capture.
• Notably, improvements were more prominent in tasks with smaller training datasets, evidencing CERT's robustness in low-resource settings by potentially mitigating overfitting through enriched sentence-level representations.

Implications and Future Directions

The theoretical implication of CERT lies in providing a pathway for LLMs to benefit from richer semantic embeddings, extending beyond token-level information. Practically, CERT establishes a framework flexible enough to integrate with various pretrained models, and future adaptions could involve its application to models like XLNet or RoBERTa to assess the cross-compatibility of the CSSL paradigm.

Future explorations might involve more sophisticated augmentation techniques or enhanced contrastive objectives like those based on ranking. Such enhancements could further refine the model's capacity to discern nuanced semantic variations, contributing to the ongoing development of more semantically aware LLMs.

In conclusion, the proposed CERT framework offers a promising direction for NLP research, emphasizing the critical importance of capturing sentence-level semantics through innovative contrastive learning strategies. The achievements and findings from this paper will likely inform and inspire subsequent research efforts aimed at fine-tuning language understanding in artificial intelligence systems.