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20192026
most citedHomograph Disambiguation Through Selective Diacritic Restoration

20 citations · 37 across the 9 of their papers we have counts for

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10 papers · 1 filter

cs.CL2026

Morphemes Without Borders: Evaluating Root-Pattern Morphology in Arabic Tokenizers and LLMs

Yara Alakeel, Chatrine Qwaider, Hanan Aldarmaki +1

This work investigates how effectively large language models (LLMs) and their tokenization schemes represent and generate Arabic root-pattern morphology, probing whether they captu…

cs.CL2026

Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models

Sawsan Alqahtani, Mir Tafseer Nayeem, Md Tahmid Rahman Laskar +2

Tokenization underlies every large language model, yet it remains an under-theorized and inconsistently designed component. Common subword approaches such as Byte Pair Encoding (BP…

cs.CL2025★ 1 cited

Beyond Fertility: Analyzing STRR as a Metric for Multilingual Tokenization Evaluation

Mir Tafseer Nayeem, Sawsan Alqahtani, Md Tahmid Rahman Laskar +2

Tokenization is a crucial but under-evaluated step in large language models (LLMs). The standard metric, fertility (the average number of tokens per word), captures compression eff…

cs.CL2024★ 2 cited

A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations

Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari +10

Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough…

cs.CL2023

Automatic Restoration of Diacritics for Speech Data Sets

Sara Shatnawi, Sawsan Alqahtani, Hanan Aldarmaki

Automatic text-based diacritic restoration models generally have high diacritic error rates when applied to speech transcripts as a result of domain and style shifts in spoken lang…

cs.CL2022★ 1 cited

Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems

Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani +2

Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data.…

cs.CL2021

Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings

Sawsan Alqahtani, Garima Lalwani, Yi Zhang +2

Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target emb…

cs.CL2020

A Multitask Learning Approach for Diacritic Restoration

Sawsan Alqahtani, Ajay Mishra, Mona Diab

In many languages like Arabic, diacritics are used to specify pronunciations as well as meanings. Such diacritics are often omitted in written text, increasing the number of possib…

cs.CL2019★ 13 cited

Efficient Convolutional Neural Networks for Diacritic Restoration

Sawsan Alqahtani, Ajay Mishra, Mona Diab

Diacritic restoration has gained importance with the growing need for machines to understand written texts. The task is typically modeled as a sequence labeling problem and current…

cs.CL2019★ 20 cited

Homograph Disambiguation Through Selective Diacritic Restoration

Sawsan Alqahtani, Hanan Aldarmaki, Mona Diab

Lexical ambiguity, a challenging phenomenon in all natural languages, is particularly prevalent for languages with diacritics that tend to be omitted in writing, such as Arabic. Om…