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Found 15 papers, 6 papers with code
Adapting Multilingual NMT to Extremely Low Resource Languages FBK’s Participation in the Basque-English Low-Resource MT Task, IWSLT 2018
In the experimental setting, an extremely low-resourced Basque-English language pair (i. e., ≈ 5. 6K in-domain training data) is our target translation task, where we considered a closely related French/Spanish-English parallel data to build the multilingual model.
Zero-Shot Neural Machine Translation with Self-Learning Cycle
Neural Machine Translation (NMT) approaches employing monolingual data are showing steady improvements in resource-rich conditions.
FBK’s Multilingual Neural Machine Translation System for IWSLT 2017
Particularly, we focus on the four zero-shot directions and show how a multilingual model trained with small data can provide reasonable results.
Jointly Optimizing Translations and Speech Timing to Improve Isochrony in Automatic Dubbing
Automatic dubbing (AD) is the task of translating the original speech in a video into target language speech.
Isochrony-Aware Neural Machine Translation for Automatic Dubbing
We introduce the task of isochrony-aware machine translation which aims at generating translations suitable for dubbing.
Isometric MT: Neural Machine Translation for Automatic Dubbing
Automatic dubbing (AD) is among the machine translation (MT) use cases where translations should match a given length to allow for synchronicity between source and target speech.
Machine Translation Verbosity Control for Automatic Dubbing
Automatic dubbing aims at seamlessly replacing the speech in a video document with synthetic speech in a different language.
Self-Learning for Zero Shot Neural Machine Translation
Neural Machine Translation (NMT) approaches employing monolingual data are showing steady improvements in resource rich conditions.
Low Resource Neural Machine Translation: A Benchmark for Five African Languages
Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks.
Adapting Multilingual Neural Machine Translation to Unseen Languages
In order to improve NMT for LRL, we employ perplexity to select HRL data that are most similar to the LRL on the basis of language distance.
Multilingual Neural Machine Translation for Zero-Resource Languages
In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine translation (MT).
Improving Zero-Shot Translation of Low-Resource Languages
Recent work on multilingual neural machine translation reported competitive performance with respect to bilingual models and surprisingly good performance even on (zeroshot) translation directions not observed at training time.
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary
Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i. e., introducing new vocabulary items if they are not included in the initial model).
Neural Machine Translation into Language Varieties
Both research and commercial machine translation have so far neglected the importance of properly handling the spelling, lexical and grammar divergences occurring among language varieties.
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation
Motivated by this, our work (i) provides a quantitative and comparative analysis of the translations produced by bilingual, multilingual and zero-shot systems; (ii) investigates the translation quality of two of the currently dominant neural architectures in MT, which are the Recurrent and the Transformer ones; and (iii) quantitatively explores how the closeness between languages influences the zero-shot translation.
