29 min

Philipp Koehn (Part 2) - How Neural Networks have Transformed Machine Translation Data Science Conversations

    • Technology

This is Part 2 of our conversation with Professor Philipp Koehn of Johns Hopkins University. Professor Koehn is one of the world’s leading experts in the field of Machine Translation and NLP.
In this episode we delve into commercial applications of machine translation, open source tools available and also take a look into what to expect in the field in the future.
Episode Summary:

Typical datasets used for training models
The role of infrastructure and technology in Machine Translation
How the academic research in Machine Translation has manifested into industry applications


Overview of what’s available in Open source tools for Machine Translation

The Future of Machine Translation and can it pass a Turing test



Resources:

Philipp Koehn latest book - Neural Machine Translation - Amazon link:

https://www.amazon.com/Neural-Machine-Translation-Philipp-Koehn/dp/1108497322 (https://www.amazon.com/Neural-Machine-Translation-Philipp-Koehn/dp/1108497322)

Omniscien Technologies - Leading Enterprise Provider of machine translation services:

https://omniscien.com/ (https://omniscien.com/)

Open Source tools:

- Fairseq https://fairseq.readthedocs.io/en/latest/ (https://fairseq.readthedocs.io/en/latest/)
- Marian https://marian-nmt.github.io/ (https://marian-nmt.github.io/)
- OpenNMT https://opennmt.net/ (https://opennmt.net/)
- Sockeye https://awslabs.github.io/sockeye/ (https://awslabs.github.io/sockeye/)

Translated texts (parallel data) for training:

- OPUS http://opus.nlpl.eu/ (http://opus.nlpl.eu/)
- Paracrawl https://paracrawl.eu/ (https://paracrawl.eu/)

Two papers mentioned about excessive use of computing power to train NLP models:

- GPT-3 https://arxiv.org/abs/2005.14165 (https://arxiv.org/abs/2005.14165)
- Roberta https://arxiv.org/abs/1907.11692 (https://arxiv.org/abs/1907.11692)

This is Part 2 of our conversation with Professor Philipp Koehn of Johns Hopkins University. Professor Koehn is one of the world’s leading experts in the field of Machine Translation and NLP.
In this episode we delve into commercial applications of machine translation, open source tools available and also take a look into what to expect in the field in the future.
Episode Summary:

Typical datasets used for training models
The role of infrastructure and technology in Machine Translation
How the academic research in Machine Translation has manifested into industry applications


Overview of what’s available in Open source tools for Machine Translation

The Future of Machine Translation and can it pass a Turing test



Resources:

Philipp Koehn latest book - Neural Machine Translation - Amazon link:

https://www.amazon.com/Neural-Machine-Translation-Philipp-Koehn/dp/1108497322 (https://www.amazon.com/Neural-Machine-Translation-Philipp-Koehn/dp/1108497322)

Omniscien Technologies - Leading Enterprise Provider of machine translation services:

https://omniscien.com/ (https://omniscien.com/)

Open Source tools:

- Fairseq https://fairseq.readthedocs.io/en/latest/ (https://fairseq.readthedocs.io/en/latest/)
- Marian https://marian-nmt.github.io/ (https://marian-nmt.github.io/)
- OpenNMT https://opennmt.net/ (https://opennmt.net/)
- Sockeye https://awslabs.github.io/sockeye/ (https://awslabs.github.io/sockeye/)

Translated texts (parallel data) for training:

- OPUS http://opus.nlpl.eu/ (http://opus.nlpl.eu/)
- Paracrawl https://paracrawl.eu/ (https://paracrawl.eu/)

Two papers mentioned about excessive use of computing power to train NLP models:

- GPT-3 https://arxiv.org/abs/2005.14165 (https://arxiv.org/abs/2005.14165)
- Roberta https://arxiv.org/abs/1907.11692 (https://arxiv.org/abs/1907.11692)

29 min

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