Aug 1, 2021 · We gener- ate dense embeddings for 29 languages using a denoising autoencoder, and evaluate the em- beddings using the World Atlas of Language.
Jun 3, 2021 · We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks.
Cross-lingual language tasks typically requirea substantial amount of annotated data or par-allel translation data.
Language embeddings have the potential to con- tribute to our understanding of language and lin- guistic typology, and to improve the performance of downstream ...
Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations ...
This work generates dense embeddings for 29 languages using a denoising autoencoder, and evaluates the embedDings using the World Atlas of Language ...
This study explores a method of waste classification using deep learning, specifically employing the Convolutional Neural Network (CNN). This research involves ...
In this paper, we study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for ...
We propose a framework for constructing a large-scale synset graph and learning language representations with node embedding algorithms. ...
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