SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods

• URL: http://arxiv.org/abs/2505.23714v2
• Date: Tue, 22 Jul 2025 17:15:48 GMT
• Title: SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods
• Authors: Roksana Goworek, Harpal Karlcut, Muhammad Shezad, Nijaguna Darshana, Abhishek Mane, Syam Bondada, Raghav Sikka, Ulvi Mammadov, Rauf Allahverdiyev, Sriram Purighella, Paridhi Gupta, Muhinyia Ndegwa, Haim Dubossarsky,
• Abstract summary: We release datasets of sentences containing polysemous words across ten low-resource languages.<n>To facilitate dataset creation, the paper presents a demonstrably beneficial semi-automatic annotation method.<n>Results highlight the importance of targeted dataset creation and evaluation for effective polysemy disambiguation.
• Score: 1.2091341579150698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the critical need for high-quality evaluation datasets in low-resource languages to advance cross-lingual transfer. While cross-lingual transfer offers a key strategy for leveraging multilingual pretraining to expand language technologies to understudied and typologically diverse languages, its effectiveness is dependent on quality and suitable benchmarks. We release new sense-annotated datasets of sentences containing polysemous words, spanning ten low-resource languages across diverse language families and scripts. To facilitate dataset creation, the paper presents a demonstrably beneficial semi-automatic annotation method. The utility of the datasets is demonstrated through Word-in-Context (WiC) formatted experiments that evaluate transfer on these low-resource languages. Results highlight the importance of targeted dataset creation and evaluation for effective polysemy disambiguation in low-resource settings and transfer studies. The released datasets and code aim to support further research into fair, robust, and truly multilingual NLP.

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