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Philipp Koehn (Part 2) - How Neural Networks have Transformed Machine Translation
Manage episode 297999657 series 2954151
Konten disediakan oleh Damien Deighan and Philipp Diesinger, Damien Deighan, and Philipp Diesinger. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Damien Deighan and Philipp Diesinger, Damien Deighan, and Philipp Diesinger atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.
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 & 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
Omniscien Technologies - Leading Enterprise Provider of machine translation services:
Open Source tools:
- Fairseq https://fairseq.readthedocs.io/en/latest/
- Marian https://marian-nmt.github.io/
- OpenNMT https://opennmt.net/
- Sockeye https://awslabs.github.io/sockeye/
Translated texts (parallel data) for training:
- OPUS http://opus.nlpl.eu/
- Paracrawl https://paracrawl.eu/
Two papers mentioned about excessive use of computing power to train NLP models:
- GPT-3 https://arxiv.org/abs/2005.14165
- Roberta https://arxiv.org/abs/1907.11692
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