Alternatives to Multilingual BERT

Multilingual BERT is a transformer-based language representation model pre-trained on a large corpus of text from many languages. It is used in research and practice for cross-lingual transfer learning, allowing a single model to perform tasks like text classification, question answering, and named entity recognition across diverse linguistic backgrounds.

At a glance

Executive summary

Multilingual BERT (mBERT) is a powerful pre-trained language model designed to understand and generate text in over 100 languages. It extends the BERT architecture by training on a massive multilingual corpus, enabling it to perform well on various downstream NLP tasks across different languages without explicit language-specific fine-tuning.

TL;DR

If you need a single model for many languages, use Multilingual BERT; if you need a language-specific tokenization strategy, consider subword tokenization as a component.

Key points

Consider the number of languages you need to support: mBERT excels with many languages in one model.
Evaluate the need for language-specific nuances: mBERT's shared vocabulary might not capture all language-specific subword patterns.
Assess computational resources: mBERT is a large model, but it avoids training separate models per language.
Determine the importance of tokenization granularity: subword tokenization is a fundamental technique used within mBERT, but can also be applied independently.
Think about the availability of pre-trained models: mBERT offers a readily available solution for multilingual NLP.

Our Take

### Our Take The advent of Multilingual BERT (mBERT) has transformed the landscape of natural language processing (NLP) by enabling the training of a single model on multiple languages. One of the key innovations that mBERT employs is subword tokenization, specifically the WordPiece algorithm. This approach allows the model to handle a vast vocabulary without requiring a separate model for each language, thus addressing the challenges posed by low-resource languages and morphological richness. Research by Devlin et al. (2018) demonstrates that mBERT achieves state-of-the-art performance across various multilingual benchmarks, outperforming previous models that relied on language-specific embeddings. The use of subword tokenization is crucial here; it breaks down words into smaller units, allowing the model to effectively learn from limited data. For instance, in languages with rich morphology, such as Turkish or Finnish, subword tokenization enables mBERT to capture nuanced meanings that would otherwise be lost with traditional word-based approaches. Furthermore, a study by Pires et al. (2019) highlights that mBERT's subword tokenization significantly reduces the out-of-vocabulary (OOV) rate, which is particularly beneficial for languages with diverse dialects or those that are less represented in training datasets. This adaptability not only enhances the model's robustness but also democratizes access to advanced NLP tools across different linguistic communities. In conclusion, Multilingual BERT, powered by subword tokenization, represents a significant leap forward in making NLP more inclusive and effective. By bridging language barriers and accommodating linguistic diversity, mBERT sets a new standard for future multilingual models, ensuring that no language is left behind in the age of artificial intelligence.

Alternative	Difference	Papers (with Multilingual BERT)	Avg viability
subword tokenization	—	1	—

Alternative

Difference

Papers (with Multilingual BERT)

Avg viability

subword tokenization

—