@inproceedings{hu-etal-2019-texar,
title = "{T}exar: A Modularized, Versatile, and Extensible Toolkit for Text Generation",
author = "Hu, Zhiting and
Shi, Haoran and
Tan, Bowen and
Wang, Wentao and
Yang, Zichao and
Zhao, Tiancheng and
He, Junxian and
Qin, Lianhui and
Wang, Di and
Ma, Xuezhe and
Liu, Zhengzhong and
Liang, Xiaodan and
Zhu, Wanrong and
Sachan, Devendra and
Xing, Eric",
editor = "Costa-juss{\`a}, Marta R. and
Alfonseca, Enrique",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-3027",
doi = "10.18653/v1/P19-3027",
pages = "159--164",
abstract = "We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled or plugged in/swapped out. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at \url{https://www.texar.io}.",
}
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<abstract>We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled or plugged in/swapped out. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.</abstract>
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%0 Conference Proceedings
%T Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
%A Hu, Zhiting
%A Shi, Haoran
%A Tan, Bowen
%A Wang, Wentao
%A Yang, Zichao
%A Zhao, Tiancheng
%A He, Junxian
%A Qin, Lianhui
%A Wang, Di
%A Ma, Xuezhe
%A Liu, Zhengzhong
%A Liang, Xiaodan
%A Zhu, Wanrong
%A Sachan, Devendra
%A Xing, Eric
%Y Costa-jussà, Marta R.
%Y Alfonseca, Enrique
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F hu-etal-2019-texar
%X We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled or plugged in/swapped out. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.
%R 10.18653/v1/P19-3027
%U https://aclanthology.org/P19-3027
%U https://doi.org/10.18653/v1/P19-3027
%P 159-164
Markdown (Informal)
[Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation](https://aclanthology.org/P19-3027) (Hu et al., ACL 2019)
ACL
- Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, and Eric Xing. 2019. Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 159–164, Florence, Italy. Association for Computational Linguistics.