· 2014
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- 24,614citations
- 2014year
Short summary
This paper introduces an RNN encoder-decoder model that learns to represent phrases and translate them, outperforming phrase-based statistical machine translation (SMT) on a French-to-English task.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduced an RNN encoder-decoder model for machine translation.
- Learns to represent phrases and their translations directly.
- Achieved a BLEU score of 34.5 on French-to-English translation.
- Outperformed phrase-based SMT by 3.1 BLEU points.
AI-generated from the title and abstract; the full text is not read.
Abstract
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014.
The authors' abstract, as published at the source., 2014 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science