Speechless Recognition: Can AI Transcribe a Language It Has Never Heard?
Breakthroughs in multilingual speech models are rapidly expanding language coverage for ASR. But what about endangered languages with little to no training data?
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Breakthroughs in multilingual speech models are rapidly expanding language coverage for ASR. But what about endangered languages with little to no training data?
Unlike some fields in machine learning, machine translation still requires large sets of training data. The solution? Creating more data when none (or not enough) exists.
New research from IBM and UC San Diego explores synthetic parallel data as a means of pre-training machine translation models — with promising results.
NeuralSpace CEO, Felix Laumann, joins the pod to discuss how they overcome the lack of research, data, and tools when training NLP models in low-resource languages.
Part of the billions of dollars going into the Metaverse will undoubtedly go into these two areas of machine translation. Meta AI reveals its MT roadmap and the people working on it.
MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) derives algorithm to handle lack of training data, making it easier to adopt current machine translation methods.
The new machine translation model, trained on 2,200 language directions, builds on Facebook’s past research on parallel sentences not aligned with English.
US Department of Homeland Security awards California startup in Phase 1 of RFP for “portable language translator.” Call for tenders still open until February 2021.
Collaboration between Amazon, Facebook, Google, Microsoft, etc., and Translators Without Borders aims to fight Covid-19 infodemic through machine translation research.
US Department of Homeland Security launches RFP for “portable language translator” device to replace “on-scene human translator.” Budget: USD 800,000.
The world’s largest NLP conference, EMNLP, announces the winners of the 2019 awards for Best Paper, Best Paper Runner-Up, Best Demo Paper and Best Resource Paper.
Slator’s 2019 report on the US healthcare interpreting market, analysis of the competitive landscape, buyer job titles, major hospital systems, and more actionable insights.
New research by Google AI explores multilingual NMT on an unprecedented scale. Good for low-resource languages, less so for the rest, results show.
At SlatorCon London 2019, Systran CEO Jean Senellart outlined the latest in NMT developments, called out some myths from vendors, and pointed out what’s missing from the industry.
EU’s CEF grants 5 project proposals for the implementation, data collection, and integration of NMT for low-resource languages, a total of EUR 4.3m in funds.
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