Claim · 2014-06-03 · publication · Stated in source · Supported

The paper proposes a neural network model called RNN Encoder–Decoder, consisting of two recurrent neural networks.

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What it connects

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation → states its contribution → The paper proposes a neural network model called RNN Encoder–Decoder, consisting of two recurrent neural networks.

Where it appears

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Claim and evidence

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translationstates its contribution ↓The paper proposes a neural network model called RNN Encoder–Decoder, consisting of two recurrent neural networks.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2014-06-03 · publication Records dated 2014 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

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

arXiv · 2014-06-03

Cite the original source

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Source excerpt

“In this paper, we propose a novel neural network model called RNN Encoder–Decoder that consists of two recurrent neural networks (RNN).”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Kyunghyun Cho; Bart van Merriënboer; Caglar Gulcehre; Fethi Bougares; Holger Schwenk; Yoshua Bengio
Version: arXiv:1406.1078v1, submitted 3 June 2014 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 71c367eca49f0cc893408d846b27b9c03b1378df2590adaba8a40ef667c7a96b). The author list is the byline as printed on the first page.
Version date: 2014-06-03
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, Yoshua Bengio, “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation”, 2014-06-03.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (21 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 3 June 2014 (UTC); later versions may word it differently.

Record ID kp-states-learning-phrase-representations-using-rnn-2014-1