mlsanityData to 14 Oct 2026

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

arXiv 1406.1078 ↗First posted 3 Jun 2014cs.CL cs.LG cs.NEcited by 4,057

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Cited by 84 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.

Key facts. Cited by 4,057 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Jun 2014.

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Citing papers per year: 2018: 2; 2019: 1; 2020: 6; 2021: 920; 2022: 728; 2023: 663; 2024: 623; 2025: 614; 2026: 500.

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