AWhat changed
Studied content-scored global attention, differentiable local-window alignment and feeding past attentional vectors into later decoder steps to improve recurrent translation models.
Scientific scope
Studied content-scored global attention, differentiable local-window alignment and feeding past attentional vectors into later decoder steps to improve recurrent translation models. Builds on earlier Bahdanau attention and recurrent sequence-to-sequence models. Learned alignment weights are not certified linguistic explanations; local-window assumptions and measured translation gains are setting-specific.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Builds on earlier Bahdanau attention and recurrent sequence-to-sequence models. Learned alignment weights are not certified linguistic explanations; local-window assumptions and measured translation gains are setting-specific.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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