Claim · 2014-09-01 · publication · Stated in source · Supported

The paper introduces an extension to the encoder–decoder model for neural machine translation that learns to align and translate jointly, as a way to address an issue it identifies with encoding a source sentence into a fixed-length vector.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

Neural Machine Translation by Jointly Learning to Align and Translate → states its contribution → The paper introduces an extension to the encoder–decoder model for neural machine translation that learns to align and translate jointly, as a way to address an issue it identifies with encoding a source sentence into a fixed-length vector.

Where it appears

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Claim and evidence

Neural Machine Translation by Jointly Learning to Align and Translatestates its contribution ↓The paper introduces an extension to the encoder–decoder model for neural machine translation that learns to align and translate jointly, as a way to address an issue it identifies with encoding a source sentence into a fixed-length vector.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2014-09-01 · 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

Neural Machine Translation by Jointly Learning to Align and Translate

arXiv · 2014-09-01

Cite the original source

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Source excerpt

“In order to address this issue, we introduce an extension to the encoder–decoder model which learns to align and translate jointly.”

Location in source arXiv v1, PDF p. 1, §1 Introduction, para. 4

Machine-checked · 2026-10-31

Source details and check notes

Authors: Dzmitry Bahdanau; Yoshua Bengio
Version: arXiv:1409.0473v1, submitted 1 September 2014 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 52391f14a0de04fdcbf2854e0349df5557ae01db0f37845e0aba96f0f1a9a2b1). The author list is the byline as printed on the first page.
Version date: 2014-09-01
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Dzmitry Bahdanau, Yoshua Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate”, 2014-09-01.
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. A cross-family machine check (2026-10-30, item B057) found the quotation at its locator. 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 1 September 2014 (UTC); later versions may word it differently.

Record ID kp-states-neural-machine-translation-by-jointly-2014-1