Cross-lingual Language Model

Author(s): Edward Ma

Originally published on Towards AI.

Discussing XLMs and unsupervised cross-lingual word embedding by multilingual neural language models

Photo by Edward Ma on Unsplash

A pre-trained model is proven to improve the downstream problem. Lample and Conneau propose two new training objectives to train cross-lingual language models (XLM). This approach leads to achieving state-of-the-art results on Cross-lingual Natural Language Inference (XNLI). On the other hand, Wada and Iwata proposed another way to learn cross-lingual text representation without parallel data. They named it Multilingual Neural Language Models.

This story will discuss Pretraining (Lample and Conneau, 2019) and Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models (Wada and Iwata, 2018)

The following are will be covered:

Data ArchitectureMultilingual Neural Language Models ArchitectureExperiment

Lample… Read the full blog for free on Medium.

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Published via Towards AI