Cloze-driven Pretraining of Self-attention Networks (1903.07785v1)

Abstract: We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems. Our model solves a cloze-style word reconstruction task, where each word is ablated and must be predicted given the rest of the text. Experiments demonstrate large performance gains on GLUE and new state of the art results on NER as well as constituency parsing benchmarks, consistent with the concurrently introduced BERT model. We also present a detailed analysis of a number of factors that contribute to effective pretraining, including data domain and size, model capacity, and variations on the cloze objective.

• The paper presents a cloze-style objective that leverages dual context to maximize token prediction accuracy.
• It employs a bi-directional transformer architecture that integrates left-to-right and right-to-left processing with multi-head self-attention.
• Empirical results show significant performance gains across GLUE, NER, and parsing tasks, underscoring the method's effectiveness.

Cloze-driven Pretraining of Self-attention Networks: A Formal Analysis

The paper "Cloze-driven Pretraining of Self-attention Networks" presents a new methodology for the pretraining of bi-directional transformer models, emphasizing the enhancement of existing LLMs via a cloze-style task. This approach aims to address key limitations in prior unidirectional and bi-directional LLMs by integrating both directions in the training of a large self-attention network inspired by LLMs, thereby enhancing performance across multiple language understanding benchmarks.

The authors introduce a pretraining scheme in which a bi-directional transformer predicts each token within a sentence by considering the full context surrounding it, effectively using a dual directional context similar to the cloze task where the model predicts ablated words. This strategy not only improves model robustness but also ensures significant performance gains on various tasks evaluated under the GLUE benchmark, achieving competitive results with state-of-the-art models like BERT, even setting new performance benchmarks in tasks such as Named Entity Recognition (NER) and constituency parsing.

Key Contributions and Results

The main contributions and findings of the paper can be summarized as follows:

• Cloze-style Objective: The model excels in predicting the center word based on surrounding context, a task aligned with human-efficient processing in natural language. This approach allows for maximizing the learning signal from the training data, as the model derives predictive power from every token.
• Bi-directional Transformer Model: The model leverages a divided framework where one tower processes the input from left-to-right and the other from right-to-left, combining outputs with a multi-head self-attention mechanism for final predictions. This dual-context processing method significantly improves model capacity compared to uni-directional frameworks.
• Empirical Evidence: Experiments reveal substantial improvements in traditional benchmarks. The model outperforms its contemporaries across various tasks within the GLUE framework, achieving a demonstrated 9.1 point performance lift over existing models in the Recognizing Textual Entailment (RTE) subset and consistently delivering robust improvements across NER and parsing tasks.
• Pretraining Factors: The paper provides an insightful exploration of factors influencing pretraining efficacy, such as the effect of data domain and volume, as well as model parameterization. Analysis reveals that cross-sentence pretraining improves efficacy notably in tasks requiring intricate contextual comprehension, and that learning gains materialize further as pretraining data scales upwards to 18 billion tokens.

Theoretical and Practical Implications

The theoretical advancements emphasize the role of bi-directional context in transforming LLM outputs into more accurate predictors of language comprehension tasks, suggesting potential for wide applicability across other computational linguistics challenges. Practically, incorporating a cloze-driven pretraining could be applied extensively in commercial LLMs where nuances of language comprehension are pivotal, offering a pathway to enhance interpretability and precision in AI-driven language applications.

The experiments highlight the utility of leveraging extensive, domain-diverse corpora for pretraining; models trained on varied data show better adaptability and task-specific performance boosts in fields like NER and parsing. This underscores the importance of broad, cross-domain training data acquisition strategies in practical implementation.

Future Directions

While the paper delineates significant strides in model accuracy and efficiency, future research could augment this architecture by investigating parameter reduction through tower-sharing methodology. Such exploration could yield deeper transform models with unchanged parameter budgets, thus pushing the envelope of model depth and efficacy in practical applications. Additionally, adaptation of training techniques to better simulate final task data could further refine the performance metrics achieved by the cloze-driven pretraining approach.

In conclusion, the development of cloze-driven pretraining methods elucidates critical pathways to enhancing language-modeling capabilities, with promising applicability in future intelligent system developments and deployments.