Language Embeddings for Typology and Cross-lingual Transfer Learning

Dian Yu; Taiqi He; Kenji Sagae

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arXiv · 2106.02082

Language Embeddings for Typology and Cross-lingual Transfer Learning

Dian Yu·Taiqi He·Kenji Sagae

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Abstract

Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks without the use of parallel data. We generate dense embeddings for 29 languages using a denoising autoencoder, and evaluate the embeddings using the World Atlas of Language Structures (WALS) and two extrinsic tasks in a zero-shot setting: cross-lingual dependency parsing and cross-lingual natural language inference.

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Dian Yu, Taiqi He, Kenji Sagae. 2021-06-03. Language Embeddings for Typology and Cross-lingual Transfer Learning. https://arxiv.org/abs/2106.02082

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