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

arXiv: https://arxiv.org/abs/2505.23714 · HTML: https://mlsanity.com/paper/2505.23714/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 74 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Towards a Unified View of Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2110.04366/) (2021)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://mlsanity.com/paper/1808.09121/) (2018)
- [MEGA: Multilingual Evaluation of Generative AI](https://mlsanity.com/paper/2303.12528/) (2023)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Multilingual Instruction Tuning With Just a Pinch of Multilinguality](https://mlsanity.com/paper/2401.01854/) (2024)
- [BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?](https://mlsanity.com/paper/2105.04949/) (2021)
- [IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages](https://mlsanity.com/paper/2403.06350/) (2024)
- [Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review](https://mlsanity.com/paper/2305.16768/) (2023)
- [AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages](https://mlsanity.com/paper/2005.00085/) (2020)
- [XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization](https://mlsanity.com/paper/2010.06478/) (2020)
- [L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages](https://mlsanity.com/paper/2211.11418/) (2022)
- [Predicting the Performance of Multilingual NLP Models](https://mlsanity.com/paper/2110.08875/) (2021)
- [Analysing The Impact Of Linguistic Features On Cross-Lingual Transfer](https://mlsanity.com/paper/2105.05975/) (2021)
- [AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples](https://mlsanity.com/paper/2104.08639/) (2021)
- [WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context](https://mlsanity.com/paper/2004.15016/) (2020)
- [Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization](https://mlsanity.com/paper/1809.04163/) (2018)
- [FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary](https://mlsanity.com/paper/2102.07983/) (2021)

## Built on by (most cited first, 4 of 4)

- [An Exploration-Analysis-Disambiguation Reasoning Framework for Word Sense Disambiguation with Low-Parameter LLMs](https://mlsanity.com/paper/2603.05400/) (2026)
- [Multilinguality Does not Make Sense: Investigating Factors Behind Zero-Shot Transfer in Sense-Aware Tasks](https://mlsanity.com/paper/2505.24834/) (2025)
- [Rethinking Metrics for Lexical Semantic Change Detection](https://mlsanity.com/paper/2602.15716/) (2026)
- [SemBench: A Universal Semantic Framework for LLM Evaluation](https://mlsanity.com/paper/2603.11687/) (2026)

## Abstract (arXiv)

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.