Mukayese: Turkish NLP Strikes Back

Safaya, Ali; Emirhan, Kurtuluş,; Goktogan, Arda; Yüret, Deniz

doi:10.18653/v1/2022.findings-acl.69

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…Despite relatively lagging progress in active Natural Language Processing (NLP) endeavors focused on Turkish, substantial advancements have been made in recent years. Mukayese stands out as a noteworthy effort, providing a comprehensive benchmarking study for various NLP tasks pertaining to the Turkish language [36]. In [37], an elaborate finite automaton model encompassing Turkish grammar rules alongside developing tools for stemming, morphological labeling, and verb negation was devised in Turkish.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

Aram,

Erdemir,

Can

2024

Applied Sciences

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People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction.

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Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

Aram,

Erdemir,

Can

2024

Applied Sciences

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“…Since the platform has been designed for Turkish, it includes tools that are specific to Turkish such as diacritic restorer. A recently-developed platform is Mukayese (Safaya et al, 2022), which is a benchmarking platform that provides a set of datasets and benchmarks for seven different types of Turkish NLP tasks. The Mukayese platform is also a part of the Turkish Data Depository (TDD) project 5 for building a repository of Turkish NLP resources.…”

Section: Related Workmentioning

confidence: 99%

TULAP - An Accessible and Sustainable Platform for Turkish Natural Language Processing Resources

Uskudarli,

Şen,

Akkurt

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrati

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Access to natural language processing resources is essential for their continuous improvement. This can be especially challenging in educational institutions where the software development effort required to package and release research outcomes may be overwhelming and under-recognized. Access to well-prepared and reliable research outcomes is important both for their developers as well as the greater research community. This paper presents an approach to address this concern with two main goals: (1) to create an opensource easily deployable platform where resources can be easily shared and explored, and (2) to use this platform to publish open-source Turkish NLP resources (datasets and tools) created by a research lab. The Turkish Natural Language Processing (TULAP) was designed and developed as an easy-to-use platform to share dataset and tool resources which supports interactive tool demos. Numerous open access Turkish NLP resources have been shared on TULAP. All tools are containerized to support portability for custom use. This paper describes the design, implementation, and deployment of TULAP with use cases (available at https://tulap.cmpe.boun.edu.tr/). A short video demonstrating our system is available at https://figshare.com/articles/ media/TULAP_Demo/22179047.

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“…We then test both the out-of-domain and in-domain procedural summarization models. Similarly, we experiment with both language-specific decoder models such as TR-BART (Safaya et al, 2022), and multilingual decoder models such as mBART and mT5 (Xue et al, 2021), described in Appendix A.3. We use the standard ROUGE metrics for evaluation.…”

Section: Summarizationmentioning

confidence: 99%

Benchmarking Procedural Language Understanding for Low-Resource Languages: A Case Study on Turkish

Uzunoglu,

Şahin

2023

Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacifi

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Understanding procedural natural language (e.g., step-by-step instructions) is a crucial step to execution and planning. However, while there are ample corpora and downstream tasks available in English, the field lacks such resources for most languages. To address this gap, we conduct a case study on Turkish procedural texts. We first expand the number of tutorials in Turkish wikiHow from 2,000 to 52,000 using automated translation tools, where the translation quality and loyalty to the original meaning are validated by a team of experts on a random set. Then, we generate several downstream tasks on the corpus, such as linking actions, goal inference, and summarization. To tackle these tasks, we implement strong baseline models via fine-tuning large language-specific models such as TR-BART and BERTurk, as well as multilingual models such as mBART, mT5, and XLM. We find that language-specific models consistently outperform their multilingual models by a significant margin across most procedural language understanding (PLU) tasks. We release our corpus, downstream tasks and the baseline models with https://github.com/ GGLAB-KU/turkish-plu.

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Mukayese: Turkish NLP Strikes Back

Cited by 12 publications

References 23 publications

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

TULAP - An Accessible and Sustainable Platform for Turkish Natural Language Processing Resources

Benchmarking Procedural Language Understanding for Low-Resource Languages: A Case Study on Turkish

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