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Papers describing Google’s Neural Machine Translation System and Text Summarization (2016) 본문
Papers describing Google’s Neural Machine Translation System and Text Summarization (2016)
yjyuwisely 2023. 9. 9. 20:36One of the best ways to truly understand the potential and application of artificial intelligence is by examining real-world systems currently in operation. For instance, in late 2016, Google unveiled its cutting-edge Neural Machine Translation System. This isn't just another research paper; this system now powers Google Translate, one of the most widely-used translation tools globally.
You can delve deeper into the technical intricacies by reading their official paper:
Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation [pdf]
If you're diving into the paper, here are some guiding questions to aid your understanding:
- Model Design: Is Google’s Neural Machine Translation System based on a sequence-to-sequence model?
- Attention Mechanism: Does the system incorporate attention mechanisms?
- If it does use attention, is it additive or multiplicative attention?
- RNN Type: What kind of RNN cell is deployed in the model?
- Bidirectional RNNs: Does the model harness bidirectional RNNs?
On the Topic of Text Summarization:
Interested readers can also explore "Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond" to understand how modern techniques are evolving the landscape of text summarization.
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