2 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
Improving the Cross-Lingual Generalisation in Visual Question Answering
Farhad Nooralahzadeh, Rico Sennrich
While several benefits were realized for multilingual vision-language pretrained models, recent benchmarks across various tasks and languages showed poor cross-lingual generalisati…
Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English
Gongbo Tang, Rico Sennrich, Joakim Nivre
Recent work has shown that deeper character-based neural machine translation (NMT) models can outperform subword-based models. However, it is still unclear what makes deeper charac…
Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models
Gongbo Tang, Rico Sennrich, Joakim Nivre
In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models. In an encoder-free model, the sums of w…
When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion
Elena Voita, Rico Sennrich, Ivan Titov
Though machine translation errors caused by the lack of context beyond one sentence have long been acknowledged, the development of context-aware NMT systems is hampered by several…
How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs
Rico Sennrich
Analysing translation quality in regards to specific linguistic phenomena has historically been difficult and time-consuming. Neural machine translation has the attractive property…