# FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary

arXiv: https://arxiv.org/abs/2102.07983 · HTML: https://mlsanity.com/paper/2102.07983/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks](https://mlsanity.com/paper/1811.01088/) (2018)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders](https://mlsanity.com/paper/2005.02590/) (2020)
- [Don't Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation](https://mlsanity.com/paper/2004.14325/) (2020)

## Built on by (most cited first, 12 of 12)

- [Do Large Language Models Understand Word Senses?](https://mlsanity.com/paper/2509.13905/) (2025)
- [Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories](https://mlsanity.com/paper/2110.14091/) (2021)
- [SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods](https://mlsanity.com/paper/2505.23714/) (2025)
- [PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search](https://mlsanity.com/paper/2207.09068/) (2022)
- [Word sense extension](https://mlsanity.com/paper/2306.05609/) (2023)
- [BERT Has Uncommon Sense: Similarity Ranking for Word Sense BERTology](https://mlsanity.com/paper/2109.09780/) (2021)
- [Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation](https://mlsanity.com/paper/2411.18337/) (2024)
- [In the LLM era, Word Sense Induction remains unsolved](https://mlsanity.com/paper/2603.11686/) (2026)
- [Reducing Target Group Bias in Hate Speech Detectors](https://mlsanity.com/paper/2112.03858/) (2021)
- [3D-EX : A Unified Dataset of Definitions and Dictionary Examples](https://mlsanity.com/paper/2308.03043/) (2023)
- [One Sense per Translation](https://mlsanity.com/paper/2106.06082/) (2021)
- [SwanNLP at SemEval-2026 Task 5: An LLM-based Framework for Plausibility Scoring in Narrative Word Sense Disambiguation](https://mlsanity.com/paper/2604.16262/) (2026)

## Abstract (arXiv)

Current models for Word Sense Disambiguation (WSD) struggle to disambiguate rare senses, despite reaching human performance on global WSD metrics. This stems from a lack of data for both modeling and evaluating rare senses in existing WSD datasets. In this paper, we introduce FEWS (Few-shot Examples of Word Senses), a new low-shot WSD dataset automatically extracted from example sentences in Wiktionary. FEWS has high sense coverage across different natural language domains and provides: (1) a large training set that covers many more senses than previous datasets and (2) a comprehensive evaluation set containing few- and zero-shot examples of a wide variety of senses. We establish baselines on FEWS with knowledge-based and neural WSD approaches and present transfer learning experiments demonstrating that models additionally trained with FEWS better capture rare senses in existing WSD datasets. Finally, we find humans outperform the best baseline models on FEWS, indicating that FEWS will support significant future work on low-shot WSD.