UzAlign v1.1.1: A Morphologically Annotated Uzbek–English Parallel Corpus with Model-Assisted Word Alignment

The scarcity of token-level verified bilingual word-alignment resources for the Uzbek language limits opportunities for evaluating alignment models and advancing cross-linguistic research. In this study, the UzAlign Gold Standard word-alignment corpus for the English–Uzbek language pair was created; it comprises 1471 parallel sentence pairs sourced from three different origins and is divided into TRAIN, DEV, and TEST sets. A five-stage annotation process was employed to verify token-level correspondences, involving the use of a bilingual dictionary, morphological analysis, identification of compound verbs, verification of low-confidence links, and the recovery of missing alignments. Model-proposed alignments were reviewed by two independent annotators, with disagreements resolved through adjudication. The corpus was enriched with lemmas, UPOS tags, Universal Dependencies morphological features, and morpheme segmentation. Compared with the final adjudicated Gold reference, the initial alignment proposals showed 98.38% Precision, 99.86% Recall, 99.12% F1, and an AER of 0.88%. These values reflect the degree of agreement between the initial proposals and the human-verified Gold reference and are not interpreted as an independent evaluation of model generalization. Pair-level decision agreement was 97.96% (Cohen’s κ = 0.9579), while link-level agreement over the defined candidate-link universe was 99.88% (Dice = 0.9994). The majority of errors stemmed from the incorrect alignment of English copula units with Uzbek linking elements. UzAlign combines Uzbek–English token alignment with morphological annotation to provide a reusable reference resource for word alignment and other cross-linguistic NLP research.