FlauBERT: Unsupervised Language Model Pre-training for French (1912.05372v4)

Abstract: LLMs have become a key step to achieve state-of-the art results in many different NLP tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French LLMs to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.

A Critical Examination of "FlauBERT: Unsupervised LLM Pre-training for French"

The paper "FlauBERT: Unsupervised LLM Pre-training for French" presents a significant contribution to the domain of NLP for languages other than English, specifically targeting the French language. The authors introduce FlauBERT, a LLM that utilizes pre-training on a diverse and extensive corpus to advance the understanding and processing of the French language in various NLP tasks. This paper aligns with the broader trend of leveraging pre-trained unsupervised LLMs like BERT, but adapts the approach for the French language.

Methodology and Dataset

The authors undertake the task of pre-training a French LLM to address the limitation of English-centric LLMs like BERT and GPT, ensuring better applicability and performance in French NLP tasks. The FlauBERT model was trained using the CNRS Jean Zay supercomputer on a corpus comprising 24 sub-corpora, which includes diverse genres ranging from formal texts like books and newspapers to informal text crawled from the internet. In total, the training corpus after filtering was approximately 71 GB in size.

Model Architecture and Training

The architecture of FlauBERT is consistent with the multi-layer bidirectional Transformer mechanism as seen in BERT. Unlike previous models, the authors employ a masked LLM (MLM) objective for training, intentionally forgoing the next sentence prediction task, in line with successful strategies adopted in models like RoBERTa. The training involved the optimization of the model with techniques such as pre-norm attention and stochastic depths, which proved effective in stabilizing the training of large Transformer models.

Two versions of FlauBERT were developed: FlauBERT, with 138 million parameters, and a larger FlauBERT model with 373 million parameters, highlighting the scalability of their approach.

Evaluation: FLUE Benchmark

The authors introduce FLUE (French Language Understanding Evaluation), a comprehensive benchmark analogous to GLUE, tailored for evaluating French LLMs. This benchmark encompasses a diverse array of tasks, including text classification, paraphrasing, natural language inference, and syntactic parsing, as well as word sense disambiguation tasks. The paper's results demonstrate that FlauBERT consistently outperforms mBERT and is competitive with CamemBERT across these tasks.

Results and Discussion

The empirical results illustrate FlauBERT's effectiveness over multilingual models in specific French contexts. For text classification tasks, FlauBERT achieved state-of-the-art results, showcasing the benefits of language-specific pre-training. Even in more nuanced tasks like word sense disambiguation, FlauBERT displayed robust performance, further affirming its utility.

The paper also suggests that while FlauBERT and CamemBERT are comparable, their complementary strengths were evidenced in ensemble evaluations, leading to improved performance metrics. This underscores a critical insight into the potential for synergistic performance gains through the ensemble of monolingual models.

Implications and Future Work

The introduction of FlauBERT represents an advancement in the adaptation of NLP systems to French, offering a foundation for further research and applications in trans-lingual NLP. As the field progresses, the model iterates on the importance of linguistic-specific adaptations of pre-trained models, a perspective that can be extended to other languages.

Future work could explore integrating cross-lingual transfer learning capabilities, potentially allowing FlauBERT to contribute to more universal LLMs. Further, the adaptation of similar methodologies to low-resource languages could leverage community-driven datasets akin to FLUE, thus broadening the impact of this research.

In conclusion, "FlauBERT: Unsupervised LLM Pre-training for French" makes a valuable addition to the resources for French NLP, paving the way for linguistically nuanced artificial intelligence applications while critically addressing the gaps in non-English language processing.