# LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)

arXiv: https://arxiv.org/abs/1906.10002 · HTML: https://mlsanity.com/paper/1906.10002/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Fantastic Semantics and Where to Find Them: Investigating Which Layers of Generative LLMs Reflect Lexical Semantics](https://mlsanity.com/paper/2403.01509/) (2024)
- [LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond](https://mlsanity.com/paper/2105.12449/) (2021)
- [Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy](https://mlsanity.com/paper/2110.02204/) (2021)
- [TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation](https://mlsanity.com/paper/2104.04632/) (2021)
- [WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks](https://mlsanity.com/paper/2107.14352/) (2021)
- [Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained Static Sense Embeddings](https://mlsanity.com/paper/2305.19092/) (2023)
- [On the Curious Case of ℓ2 norm of Sense Embeddings](https://mlsanity.com/paper/2210.14815/) (2022)

## Abstract (arXiv)

This paper describes the LIAAD system that was ranked second place in the Word-in-Context challenge (WiC) featured in SemDeep-5. Our solution is based on a novel system for Word Sense Disambiguation (WSD) using contextual embeddings and full-inventory sense embeddings. We adapt this WSD system, in a straightforward manner, for the present task of detecting whether the same sense occurs in a pair of sentences. Additionally, we show that our solution is able to achieve competitive performance even without using the provided training or development sets, mitigating potential concerns related to task overfitting