BERTrade: Using Contextual Embeddings to Parse Old French - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

BERTrade: Using Contextual Embeddings to Parse Old French


The successes of contextual word embeddings learned by training large-scale language models, while remarkable, have mostly occurred for languages where significant amounts of raw texts are available and where annotated data in downstream tasks have a relatively regular spelling. Conversely, it is not yet completely clear if these models are also well suited for lesser-resourced and more irregular languages. We study the case of Old French, which is in the interesting position of having relatively limited amount of available raw text, but enough annotated resources to assess the relevance of contextual word embedding models for downstream NLP tasks. In particular, we use POS-tagging and dependency parsing to evaluate the quality of such models in a large array of configurations, including models trained from scratch from small amounts of raw text and models pre-trained on other languages but fine-tuned on Medieval French data.
Fichier principal
Vignette du fichier
Bertrade_LREC_2022.pdf (536.81 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03736840 , version 1 (22-07-2022)


Attribution - NonCommercial


  • HAL Id : hal-03736840 , version 1


Loïc Grobol, Mathilde Regnault, Pedro Ortiz Suarez, Benoît Sagot, Laurent Romary, et al.. BERTrade: Using Contextual Embeddings to Parse Old French. 13th Language Resources and Evaluation Conference, European Language Resources Association, Jun 2022, Marseille, France. ⟨hal-03736840⟩
162 View
105 Download


Gmail Facebook X LinkedIn More