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Found 4 papers, 1 papers with code
Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content
This work compares the performances achieved by Phrase-Based Statistical Machine Translation systems (PB-SMT) and attention-based Neuronal Machine Translation systems (NMT) when translating User Generated Content (UGC), as encountered in social medias, from French to English.
LIMSI @ WMT 2020
This paper describes LIMSI’s submissions to the translation shared tasks at WMT’20.
Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models
This work explores the capacities of character-based Neural Machine Translation to translate noisy User-Generated Content (UGC) with a strong focus on exploring the limits of such approaches to handle productive UGC phenomena, which almost by definition, cannot be seen at training time.
Understanding the Impact of UGC Specificities on Translation Quality
This work takes a critical look at the evaluation of user-generated content automatic translation, the well-known specificities of which raise many challenges for MT.
