# Context-Gloss Augmentation for Improving Arabic Target Sense Verification

arXiv: https://arxiv.org/abs/2302.03126 · HTML: https://mlsanity.com/paper/2302.03126/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [Entity Linking in 100 Languages](https://mlsanity.com/paper/2011.02690/) (2020)
- [A Panoramic Survey of Natural Language Processing in the Arab World](https://mlsanity.com/paper/2011.12631/) (2020)
- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [The Arabic Ontology -- An Arabic Wordnet with Ontologically Clean Content](https://mlsanity.com/paper/2205.09664/) (2022)
- [ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD](https://mlsanity.com/paper/2205.09685/) (2022)
- [Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences](https://mlsanity.com/paper/2009.11795/) (2020)
- [WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context](https://mlsanity.com/paper/2004.15016/) (2020)
- [LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation](https://mlsanity.com/paper/2104.08110/) (2021)
- [WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks](https://mlsanity.com/paper/2107.14352/) (2021)
- [Context-gloss Augmentation for Improving Word Sense Disambiguation](https://mlsanity.com/paper/2110.07174/) (2021)
- [Building on Huang et al. GlossBERT for Word Sense Disambiguation](https://mlsanity.com/paper/2112.07089/) (2021)

## Built on by (most cited first, 6 of 6)

- [AraFinNLP 2024: The First Arabic Financial NLP Shared Task](https://mlsanity.com/paper/2407.09818/) (2024)
- [SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarks](https://mlsanity.com/paper/2310.19029/) (2023)
- [Qabas: An Open-Source Arabic Lexicographic Database](https://mlsanity.com/paper/2406.06598/) (2024)
- [NLU-STR at SemEval-2024 Task 1: Generative-based Augmentation and Encoder-based Scoring for Semantic Textual Relatedness](https://mlsanity.com/paper/2405.00659/) (2024)
- [ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task](https://mlsanity.com/paper/2407.20663/) (2024)
- [SinaTools: Open Source Toolkit for Arabic Natural Language Processing](https://mlsanity.com/paper/2411.01523/) (2024)

## Abstract (arXiv)

Arabic language lacks semantic datasets and sense inventories. The most common semantically-labeled dataset for Arabic is the ArabGlossBERT, a relatively small dataset that consists of 167K context-gloss pairs (about 60K positive and 107K negative pairs), collected from Arabic dictionaries. This paper presents an enrichment to the ArabGlossBERT dataset, by augmenting it using (Arabic-English-Arabic) machine back-translation. Augmentation increased the dataset size to 352K pairs (149K positive and 203K negative pairs). We measure the impact of augmentation using different data configurations to fine-tune BERT on target sense verification (TSV) task. Overall, the accuracy ranges between 78% to 84% for different data configurations. Although our approach performed at par with the baseline, we did observe some improvements for some POS tags in some experiments. Furthermore, our fine-tuned models are trained on a larger dataset covering larger vocabulary and contexts. We provide an in-depth analysis of the accuracy for each part-of-speech (POS).