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20192026
most citedData Augmentation via Dependency Tree Morphing for Low-Resource Languages

5 citations · 5 across the 12 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CL2024

GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and Correction

Ali Gebeşçe, Gözde Gül Şahin

Sophisticated grammatical error detection/correction tools are available for a small set of languages such as English and Chinese. However, it is not straightforward -- if not impo…

cs.CL2024

Instructional Text Across Disciplines: A Survey of Representations, Downstream Tasks, and Open Challenges Toward Capable AI Agents

Abdulfattah Safa, Tamta Kapanadze, Arda Uzunoğlu +1

Recent advances in large language models have demonstrated promising capabilities in following simple instructions through instruction tuning. However, real-world tasks often invol…

cs.CL2024

No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study

Gürkan Soykan, Gözde Gül Şahin

Multilingual instruction tuning (MIT) is challenged by the curse of multilinguality, data scarcity, and high computational cost. A natural hypothesis is that carefully selecting a…

cs.CL2024

A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding

Abdulfattah Safa, Gözde Gül Şahin

Dialogue State Tracking (DST) is crucial for understanding user needs and executing appropriate system actions in task-oriented dialogues. Majority of existing DST methods are desi…

cs.CL2024

PARADISE: Evaluating Implicit Planning Skills of Language Models with Procedural Warnings and Tips Dataset

Arda Uzunoglu, Abdalfatah Rashid Safa, Gözde Gül Şahin

Recently, there has been growing interest within the community regarding whether large language models are capable of planning or executing plans. However, most prior studies use L…

cs.AI2024

Quantifying Divergence for Human-AI Collaboration and Cognitive Trust

Müge Kural, Ali Gebeşçe, Tilek Chubakov +1

Predicting the collaboration likelihood and measuring cognitive trust to AI systems is more important than ever. To do that, previous research mostly focus solely on the model feat…