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    <abstract>We introduce YATO, an open-source, easy-to-use toolkit for text analysis with deep learning. Different from existing heavily engineered toolkits and platforms, YATO is lightweight and user-friendly for researchers from cross-disciplinary areas. Designed in a hierarchical structure, YATO supports free combinations of three types of widely used features including 1) traditional neural networks (CNN, RNN, etc.); 2) pre-trained language models (BERT, RoBERTa, ELECTRA, etc.); and 3) user-customized neural features via a simple configurable file. Benefiting from the advantages of flexibility and ease of use, YATO can facilitate fast reproduction and refinement of state-of-the-art NLP models, and promote the cross-disciplinary applications of NLP techniques. The code, examples, and documentation are publicly available at https://github.com/jiesutd/YATO. A demo video is also available at https://www.youtube.com/playlist?list=PLJ0mhzMcRuDUlTkzBfAftOqiJRxYTTjXH.</abstract>
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%0 Conference Proceedings
%T YATO: Yet Another deep learning based Text analysis Open toolkit
%A Wang, Zeqiang
%A Wang, Yile
%A Wu, Jiageng
%A Teng, Zhiyang
%A Yang, Jie
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-yato
%X We introduce YATO, an open-source, easy-to-use toolkit for text analysis with deep learning. Different from existing heavily engineered toolkits and platforms, YATO is lightweight and user-friendly for researchers from cross-disciplinary areas. Designed in a hierarchical structure, YATO supports free combinations of three types of widely used features including 1) traditional neural networks (CNN, RNN, etc.); 2) pre-trained language models (BERT, RoBERTa, ELECTRA, etc.); and 3) user-customized neural features via a simple configurable file. Benefiting from the advantages of flexibility and ease of use, YATO can facilitate fast reproduction and refinement of state-of-the-art NLP models, and promote the cross-disciplinary applications of NLP techniques. The code, examples, and documentation are publicly available at https://github.com/jiesutd/YATO. A demo video is also available at https://www.youtube.com/playlist?list=PLJ0mhzMcRuDUlTkzBfAftOqiJRxYTTjXH.
%R 10.18653/v1/2023.emnlp-demo.11
%U https://aclanthology.org/2023.emnlp-demo.11
%U https://doi.org/10.18653/v1/2023.emnlp-demo.11
%P 131-139

Markdown (Informal)

[YATO: Yet Another deep learning based Text analysis Open toolkit](https://aclanthology.org/2023.emnlp-demo.11) (Wang et al., EMNLP 2023)

• YATO: Yet Another deep learning based Text analysis Open toolkit (Wang et al., EMNLP 2023)

ACL

• Zeqiang Wang, Yile Wang, Jiageng Wu, Zhiyang Teng, and Jie Yang. 2023. YATO: Yet Another deep learning based Text analysis Open toolkit. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 131–139, Singapore. Association for Computational Linguistics.