Texygen: A Benchmarking Platform for Text Generation Models
| Autor: | Yaoming Zhu, Weinan Zhang, Sidi Lu, Jiaxian Guo, Lei Zheng, Jun Wang, Yong Yu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language business.industry Computer science media_common.quotation_subject 02 engineering and technology Benchmarking Machine Learning (cs.LG) Computer Science - Information Retrieval Set (abstract data type) Consistency (database systems) Computer Science - Learning Work (electrical) 020204 information systems 0202 electrical engineering electronic engineering information engineering Text generation 020201 artificial intelligence & image processing Quality (business) Software engineering business Computation and Language (cs.CL) Information Retrieval (cs.IR) Reliability (statistics) media_common |
| Zdroj: | SIGIR |
| Popis: | We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality and the consistency of the generated texts. The Texygen platform could help standardize the research on text generation and facilitate the sharing of fine-tuned open-source implementations among researchers for their work. As a consequence, this would help in improving the reproductivity and reliability of future research work in text generation. 4 pages |
| Databáze: | OpenAIRE |
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