ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding

Abstract: Coarse-grained linguistic information, such as named entities or phrases, facilitates adequately representation learning in pre-training. Previous works mainly focus on extending the objective of BERT's Masked Language Modeling (MLM) from masking individual tokens to contiguous sequences of n tokens. We argue that such contiguously masking method neglects to model the intra-dependencies and inter-relation of coarse-grained linguistic information. As an alternative, we propose ERNIE-Gram, an explicitly n-gram masking method to enhance the integration of coarse-grained information into pre-training. In ERNIE-Gram, n-grams are masked and predicted directly using explicit n-gram identities rather than contiguous sequences of n tokens. Furthermore, ERNIE-Gram employs a generator model to sample plausible n-gram identities as optional n-gram masks and predict them in both coarse-grained and fine-grained manners to enable comprehensive n-gram prediction and relation modeling. We pre-train ERNIE-Gram on English and Chinese text corpora and fine-tune on 19 downstream tasks. Experimental results show that ERNIE-Gram outperforms previous pre-training models like XLNet and RoBERTa by a large margin, and achieves comparable results with state-of-the-art methods. The source codes and pre-trained models have been released at https://github.com/PaddlePaddle/ERNIE.

• The paper introduces explicit n-gram masking to reduce prediction space and capture detailed coarse-grained semantics.
• It employs comprehensive n-gram prediction, integrating both fine- and coarse-grained information for improved model accuracy.
• Enhanced n-gram relation modeling boosts performance on key tasks like named entity recognition and question answering.

Overview of ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding

Introduction

The paper introduces ERNIE-Gram, a novel approach to LLM pre-training that leverages explicitly masked n-grams to enhance natural language understanding (NLU). This approach addresses the limitations of traditional masked LLMs (MLMs), such as BERT, which predominantly focus on individual tokens. ERNIE-Gram seeks to integrate coarse-grained linguistic information, such as phrases and named entities, into the pre-training process.

Methodology

ERNIE-Gram employs a unique methodology involving several components:

1. Explicitly N-Gram Masking: Unlike conventional MLMs that mask sequences of contiguous tokens, ERNIE-Gram masks n-grams using explicit n-gram identities. This reduces the prediction space, making it more focused and effective.
2. Comprehensive N-Gram Prediction: The model predicts masked n-grams in both coarse-grained and fine-grained manners, allowing for a more robust understanding of n-gram semantics.
3. Enhanced N-Gram Relation Modeling: A generator model samples plausible n-gram identities, enabling the main model to learn subtle semantic relationships between n-grams.

The model is pre-trained on English and Chinese corpora and fine-tuned across 19 downstream tasks.

Results

Empirical evaluations reveal that ERNIE-Gram significantly outperforms previous models, such as XLNet and RoBERTa, on benchmark tasks in NLU. Key highlights include strong performance on the GLUE benchmark and SQuAD, where ERNIE-Gram achieved notable improvements over the baseline methods.

Analysis

The use of explicitly n-gram masking allows ERNIE-Gram to maintain tighter intra-dependencies within coarse-grained text units, effectively capturing semantic details that conventional models might overlook. The n-gram relation modeling via plausible sample identities further enhances this advantage by modeling semantic pair relationships.

Implications

From a theoretical standpoint, ERNIE-Gram expands the possibilities for integrating more detailed semantic information into LLMs without increasing model complexity during fine-tuning. Practically, this aligns well with tasks requiring nuanced understanding of complex linguistic structures, such as named entity recognition and question answering.

Future Directions

The success of ERNIE-Gram suggests several avenues for further research:

• Exploration of larger and more comprehensive n-gram lexicons beyond tri-grams.
• Application of ERNIE-Gram in multi-lingual contexts to assess its adaptability across diverse linguistic datasets.
• Expansion to larger model sizes to determine scaling properties and impacts on even more resource-intensive tasks.

In conclusion, ERNIE-Gram contributes a significant advancement in the integration of n-gram semantics into LLM pre-training, offering both theoretical insights and practical enhancements to current NLU challenges.