Claim · 2017-06-12 · event · Stated in source · Supported

Vaswani et al. (2017) attribute additive attention, one of the two most commonly used attention functions, to Bahdanau, Cho and Bengio (2014).

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

Attention Is All You Need → attributes to → Neural Machine Translation by Jointly Learning to Align and Translate

Where it appears

No collection page holds this claim. It is listed with its records in the explorer.

Claim and evidence

Attention Is All You Needattributes to ↓Neural Machine Translation by Jointly Learning to Align and Translate

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2017-06-12 · event Records dated 2017 →
Kind of attribution: Attributes a term or concept to the work

Scope and limitsA citation for one attention function, kept as the citing text's attribution; it does not state that Bahdanau et al. devised attention in general.

The evidence 02

primary sourceSupports the claim

Attention Is All You Need

arXiv · 2017-06-12

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“The two most commonly used attention functions are additive attention [2], and dot-product (multiplicative) attention.”

Location in source PDF p. 4, section 3.2.1

How it was read Retrieved directly from the publisher

Machine-checked · 2026-10-31

Source details and check notes

Authors: Ashish Vaswani; Noam Shazeer; Niki Parmar; Jakob Uszkoreit; Llion Jones; Aidan N. Gomez; Lukasz Kaiser; Illia Polosukhin
Version: arXiv:1706.03762v1; first-submission date from version history. Later revisions are not substituted.
Version date: 2017-06-12
Canonical address: Open the canonical source ↗
Accessed: 2026-09-20
Language: English (en)
Attribution: Ashish Vaswani; Noam Shazeer; Niki Parmar; Jakob Uszkoreit; Llion Jones; Aidan N. Gomez; Lukasz Kaiser; Illia Polosukhin, Attention Is All You Need (2017-06-12).
Reuse: No open redistribution licence asserted. Bibliographic metadata and brief attributed paraphrases only; no full source, personal contact details or dataset imported.
Check notes: Candidate pack passage, Kinds of AI and technical history (research/technical-history). The quotation (15 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes, PDF line-break hyphens and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (31 October 2026, item 108) re-read the record’s sources and found that they support what the record states. No human editorial review.

primary sourceSupports the claim

Attention Is All You Need

arXiv · 2017-06-12

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Paraphrase

Reference [2] is Bahdanau, Cho and Bengio, "Neural machine translation by jointly learning to align and translate", CoRR abs/1409.0473, 2014.

Location in source PDF p. 10, references

How it was read Retrieved directly from the publisher

Machine-checked · 2026-10-31

Source details and check notes

Authors: Ashish Vaswani; Noam Shazeer; Niki Parmar; Jakob Uszkoreit; Llion Jones; Aidan N. Gomez; Lukasz Kaiser; Illia Polosukhin
Version: arXiv:1706.03762v1; first-submission date from version history. Later revisions are not substituted.
Version date: 2017-06-12
Canonical address: Open the canonical source ↗
Accessed: 2026-09-20
Language: English (en)
Attribution: Ashish Vaswani; Noam Shazeer; Niki Parmar; Jakob Uszkoreit; Llion Jones; Aidan N. Gomez; Lukasz Kaiser; Illia Polosukhin, Attention Is All You Need (2017-06-12).
Reuse: No open redistribution licence asserted. Bibliographic metadata and brief attributed paraphrases only; no full source, personal contact details or dataset imported.
Check notes: Candidate pack passage, Kinds of AI and technical history (research/technical-history). The paraphrase is Selekoa’s, written from the text retrieved for the candidate pack on 2026-10-29; it is not a quotation and was not machine-matched. A cross-family machine check (31 October 2026, item 108) re-read the record’s sources and found that they support what the record states. 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 document expressly attributes an argument, concept, term, finding or approach to another work, in its own words; the kind of attribution is recorded. The record is the citing document’s claim. It is not evidence that the cited work originated the idea, that the attribution is accurate, or of influence beyond the passage. Citation alone is recorded as a citation.

Date. Date of submission (an arXiv version or a journal’s received date, as the source states it); not a later revision or the venue’s publication date.

Record ID th-fam-tllm-attention-credits-bahdanau