Polyglot Contextual Representations Improve Crosslingual Transfer

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427115" target="_blank" >RIV/00216208:11320/19:10427115 - isvavai.cz</a>

Result on the web

<a href="https://www.aclweb.org/anthology/N19-1392" target="_blank" >https://www.aclweb.org/anthology/N19-1392</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Polyglot Contextual Representations Improve Crosslingual Transfer

Original language description

We introduce Rosita, a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the advantages of contextual word representations with those of multilingual representation learning. We produce language models from dissimilar language pairs (English/Arabic and English/Chinese) and use them in dependency parsing, semantic role labeling, and named entity recognition, with comparisons to monolingual and non-contextual variants. Our results provide further evidence for the benefits of polyglot learning, in which representations are shared across multiple languages.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů