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Philipp Koehn (Part 2) - How Neural Networks have Transformed Machine Translation
Manage episode 297999657 series 2954151
コンテンツは Damien Deighan and Philipp Diesinger, Damien Deighan, and Philipp Diesinger によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Damien Deighan and Philipp Diesinger, Damien Deighan, and Philipp Diesinger またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.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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