92 citations · 216 across the 13 of their papers we have counts for
15 papers
MemToolAgent: Leveraging Memory for Tool Using Agents Based on Environment and User Feedback
Suleyman Armagan Er, Danilo Ribeiro, Yogesh Virkar +5
Modern large language model (LLM) agents can use external tools to help users solve complex tasks. However, for problems that require learning from long-term historical events or f…
Jointly Optimizing Translations and Speech Timing to Improve Isochrony in Automatic Dubbing
Alexandra Chronopoulou, Brian Thompson, Prashant Mathur +3
Automatic dubbing (AD) is the task of translating the original speech in a video into target language speech. The new target language speech should satisfy isochrony; that is, the…
Sockeye 3: Fast Neural Machine Translation with PyTorch
Felix Hieber, Michael Denkowski, Tobias Domhan +11
Sockeye 3 is the latest version of the Sockeye toolkit for Neural Machine Translation (NMT). Now based on PyTorch, Sockeye 3 provides faster model implementations and more advanced…
Isometric MT: Neural Machine Translation for Automatic Dubbing
Surafel M. Lakew, Yogesh Virkar, Prashant Mathur +1
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. F…
Isochrony-Aware Neural Machine Translation for Automatic Dubbing
Derek Tam, Surafel M. Lakew, Yogesh Virkar +2
We introduce the task of isochrony-aware machine translation which aims at generating translations suitable for dubbing. Dubbing of a spoken sentence requires transferring the cont…
Machine Translation Verbosity Control for Automatic Dubbing
Surafel M. Lakew, Marcello Federico, Yue Wang +4
Automatic dubbing aims at seamlessly replacing the speech in a video document with synthetic speech in a different language. The task implies many challenges, one of which is gener…
Self-Learning for Zero Shot Neural Machine Translation
Surafel M. Lakew, Matteo Negri, Marco Turchi
Neural Machine Translation (NMT) approaches employing monolingual data are showing steady improvements in resource rich conditions. However, evaluations using real-world low-resour…
Low Resource Neural Machine Translation: A Benchmark for Five African Languages
Surafel M. Lakew, Matteo Negri, Marco Turchi
Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks. In this work, we benchmark NMT between English and five…
Adapting Multilingual Neural Machine Translation to Unseen Languages
Surafel M. Lakew, Alina Karakanta, Marcello Federico +2
Multilingual Neural Machine Translation (MNMT) for low-resource languages (LRL) can be enhanced by the presence of related high-resource languages (HRL), but the relatedness of HRL…
Controlling the Output Length of Neural Machine Translation
Surafel Melaku Lakew, Mattia Di Gangi, Marcello Federico
The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be…
Multilingual Neural Machine Translation for Zero-Resource Languages
Surafel M. Lakew, Marcello Federico, Matteo Negri +1
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 t…
Improving Zero-Shot Translation of Low-Resource Languages
Surafel M. Lakew, Quintino F. Lotito, Matteo Negri +2
Recent work on multilingual neural machine translation reported competitive performance with respect to bilingual models and surprisingly good performance even on (zeroshot) transl…
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary
Surafel M. Lakew, Aliia Erofeeva, Matteo Negri +2
We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for…
Neural Machine Translation into Language Varieties
Surafel M. Lakew, Aliia Erofeeva, Marcello Federico
Both research and commercial machine translation have so far neglected the importance of properly handling the spelling, lexical and grammar divergences occurring among language va…
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation
Surafel M. Lakew, Mauro Cettolo, Marcello Federico
Recently, neural machine translation (NMT) has been extended to multilinguality, that is to handle more than one translation direction with a single system. Multilingual NMT showed…