SenWiCh: Sense-Annotated Sentences for WSD and WiC in Low-Resource Languages

SenWiCh is a multilingual dataset of sense-annotated sentences designed to support research in Word Sense Disambiguation (WSD) and Word-in-Context (WiC) tasks for low-resource languages. It covers ten typologically and geographically diverse languages: Azerbaijani, Kannada, Korean, Marathi, Polish, Punjabi, Swahili, Telugu, Urdu and Vietnamese. Each dataset contains sentences featuring polysemous words, annotated for word senses using a semi-automatic method that combines embedding-based clustering with interactive manual annotation. This approach ensures broad semantic coverage and efficient sense labeling across diverse linguistic contexts.  The release includes three main components: wic_files.zip: Contains WiC-formatted JSON files ({lang}_train.data, {lang}_dev.data, {lang}_test.data) for each language, suitable for binary classification-based training and evaluation. wsd_files.zip: Provides full sets of sense-annotated sentences for each language in CSV format ({lang}.csv), supporting fine-grained WSD tasks. sense_lists.zip: Includes CSV files ({lang}.csv) listing the polysemous lemmas, their associated senses, and unique sense IDs, useful for lexical resource analysis and alignment. The dataset creation process is described in our paper "SenWiCh: Sense-Annotation of Sentences in Low-Resource Languages for WiC using Hybrid Methods" (SIGTYP 2025, ACL). [arXiv link]A link to the ACL Anthology version will be provided as soon as it becomes available. For now, please cite the arXiv version. To support dataset creation and adaptation to new languages, we also release the interactive annotation tool used in this process: projecting_sentences. This work was partially supported by the research program Change is Key!, funded by Riksbankens Jubileumsfond (grant number M21-0021).