# Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation

arXiv: https://arxiv.org/abs/1809.00794 · HTML: https://mlsanity.com/paper/1809.00794/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2018.

## Built on by (most cited first, 13 of 13)

- [Efficient (Soft) Q-Learning for Text Generation with Limited Good Data](https://mlsanity.com/paper/2106.07704/) (2021)
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- [DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder](https://mlsanity.com/paper/2109.10480/) (2021)
- [Smaaprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning](https://mlsanity.com/paper/2110.06273/) (2021)
- [Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering](https://mlsanity.com/paper/2203.07633/) (2022)
- [Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation](https://mlsanity.com/paper/2305.00034/) (2023)
- [A Data-Centric Framework for Composable NLP Workflows](https://mlsanity.com/paper/2103.01834/) (2021)
- [Adversarial Machine Learning in Text Analysis and Generation](https://mlsanity.com/paper/2101.08675/) (2021)
- [AfriWOZ: Corpus for Exploiting Cross-Lingual Transferability for Generation of Dialogues in Low-Resource, African Languages](https://mlsanity.com/paper/2204.08083/) (2022)
- [COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation](https://mlsanity.com/paper/2607.08071/) (2026)
- [Intensive Vision-guided Network for Radiology Report Generation](https://mlsanity.com/paper/2402.03754/) (2024)

## Abstract (arXiv)

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.