Texar NLG Toolkit

A Texar NLG Toolkit is an NLG toolkit (to create NLG systems).

• See: Natural Language Generation.

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References

2018a

• https://github.com/asyml/texar/
  • QUOTE: Texar is an open-source toolkit based on Tensorflow, aiming to support a broad set of machine learning especially text generation tasks, such as machine translation, dialog, summarization, content manipulation, language modeling, and so on. Texar is designed for both researchers and practitioners for fast prototyping and experimentation.

    With the design goals of modularity, versatility, and extensibility in mind, Texar extracts the common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and facilitates arbitrary model architectures and algorithmic paradigms, e.g.,

    • encoder(s) to decoder(s), sequential- and self-attentions, memory, hierarchical models, classifiers...
    • maximum likelihood learning, reinforcement learning, adversarial learning, probabilistic modeling, ...
  • With Texar, cutting-edge complex models can be easily constructed, freely enriched with best modeling/training practices, readily fitted into standard training/evaluation pipelines, and fastly experimented and evolved by, e.g., plugging-in and swapping-out different modules.

2018b

• "Introducing Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation and Beyond." Blog post, 2018-09-18

2018c

• (Hu et al., 2018) ⇒ Zhiting Hu, Haoran Shi, Zichao Yang, Bowen Tan, Tiancheng Zhao, Junxian He, Wentao Wang, Xingjiang Yu, Lianhui Qin, Di Wang, Xuezhe Ma, Hector Liu, Xiaodan Liang, Wanrong Zhu, Devendra Singh Sachan, and Eric P. Xing. (2018). “Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation.” In: arXiv preprint arXiv:1809.00794.
  • QUOTE: We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transforms 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, losses, and learning processes are fully decomposed. Modules at high concept level can be freely assembled or plugged in / swapped out. These features make Texar particularly suitable for researchers and practitioners to do fast prototyping and experimentation, as well as foster technique sharing across different text generation tasks. We provide case studies to demonstrate the use and advantage of the toolkit. Texar is released under Apache license 2.0 at this https URL

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