All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Crosslingual Content Scoring in Five Languages Using Machine-Translation and Multilingual Transformer Models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AHPF75PD8" target="_blank" >RIV/00216208:11320/23:HPF75PD8 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/10.1007/s40593-023-00370-1" target="_blank" >https://link.springer.com/10.1007/s40593-023-00370-1</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s40593-023-00370-1" target="_blank" >10.1007/s40593-023-00370-1</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Crosslingual Content Scoring in Five Languages Using Machine-Translation and Multilingual Transformer Models

  • Original language description

    "Abstractn This paper investigates crosslingual content scoring, a scenario where scoring models trained on learner data in one language are applied to data in a different language. We analyze data in five different languages (Chinese, English, French, German and Spanish) collected for three prompts of the established English ASAP content scoring dataset. We cross the language barrier by means of both shallow and deep learning crosslingual classification models using both machine translation and multilingual transformer models. We find that a combination of machine translation and multilingual models outperforms each method individually - our best results are reached when combining the available data in different languages, i.e. first training a model on the large English ASAP dataset before fine-tuning on smaller amounts of training data in the target language."

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>ost</sub> - Miscellaneous article in a specialist periodical

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    "International Journal of Artificial Intelligence in Education"

  • ISSN

    1560-4292

  • e-ISSN

  • Volume of the periodical

    ""

  • Issue of the periodical within the volume

    2023-6-19

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    27

  • Pages from-to

    1-27

  • UT code for WoS article

  • EID of the result in the Scopus database