  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 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 and plugged in/swapped out. The toolkit
also supports a rich set of large-scale pretrained models. 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.
