Skip to Main Content (Press Enter)

Logo UNIOR
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Persone
  • Strutture

UNIFIND
Logo UNIOR

|

UNIFIND

unior.it
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Persone
  • Strutture

Towards a reliable annotation framework for crisis MT evaluation: Addressing error taxonomies and annotator agreement

Abstract
Data di Pubblicazione:
2025
Abstract:
This paper presents a detailed analysis of the annotation process used in the ITALERT (Italian
Emergency Response Text) corpus, specifically designed to evaluate the performance of neural
machine translation (NMT) systems and large language models (LLMs) in translating high-stakes
messages from Italian to English.
Tipologia CRIS:
4.2 Abstract in Atti di convegno
Keywords:
ITALERT corpis, neural machine translation
Elenco autori:
Staiano, Maria Carmen; Han, Lifeng; Monti, Johanna; Chiusaroli, Francesca
Autori di Ateneo:
MONTI JOHANNA
Link alla scheda completa:
https://unora.unior.it/handle/11574/248746
Link al Full Text:
https://unora.unior.it//retrieve/handle/11574/248746/248730/CL2025+Book+Of+Abstracts_24th+June.pdf
Titolo del libro:
Corpus Linguistics 2025
  • Dati Generali

Dati Generali

URL

https://u-pad.unimc.it/bitstream/11393/360131/1/CL2025 Book Of Abstracts_24th June.pdf
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 26.7.2.1