Cross-lingual Language Model
Last Updated on July 25, 2023 by Editorial Team
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.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
