SZTAKI HLT | Egy emBERT próbáló feladat
Dávid Márk Nemeskey
Egy emBERT próbáló feladat
In XVI. Magyar Számítógépes Nyelvészeti Konferencia, 2020

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In the last couple of years deep, contextual embeddings have superseded traditional, manually compiled feature sets in most NLP tasks. However, the Hungarian NLP pipelines (e-magyar, magyarlanc) still manual features. In this article, we introduce the emBERT module, which allows the integration of contextual embedding-based classifiers into e-magyar, via the transformers library.

The module provides classifiers for named netity recognition and NP chunking, achieving state-of-the-art performance.

Citation
@InProceedings{ Nemeskey:2020a,
  author = {Nemeskey, Dávid Márk},
  title = {Egy \texttt{emBERT} próbáló feladat},
  booktitle = {{XVI}.\ Magyar Sz{\'a}m{\'i}t{\'o}g{\'e}pes Nyelv{\'e}szeti Konferencia ({MSZNY}2020)},
  year = 2020,
  pages = {409--418},
  address = {Szeged},
}