33 citations · 53 across the 6 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…