DELTA: A DEep learning based Language Technology plAtform
Kun Han, Junwen Chen, Hui Zhang, Haiyang Xu, Yiping Peng, Yun Wang,, Ning Ding, Hui Deng, Yonghu Gao, Tingwei Guo, Yi Zhang, Yahao He, Baochang, Ma, Yulong Zhou, Kangli Zhang, Chao Liu, Ying Lyu, Chenxi Wang, Cheng Gong,, Yunbo Wang, Wei Zou, Hui Song, and Xiangang Li

TL;DR
DELTA is an end-to-end deep learning platform that simplifies development, training, and deployment of natural language and speech processing models for industry and academic applications.
Contribution
It introduces a comprehensive platform integrating neural network models and deployment tools, enabling efficient development and deployment of NLP and speech models.
Findings
Reliable performance on multiple NLP and speech tasks
Used to develop state-of-the-art algorithms
Deployed in real production serving millions
Abstract
In this paper we present DELTA, a deep learning based language technology platform. DELTA is an end-to-end platform designed to solve industry level natural language and speech processing problems. It integrates most popular neural network models for training as well as comprehensive deployment tools for production. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. We demonstrate the reliable performance with DELTA on several natural language processing and speech tasks, including text classification, named entity recognition, natural language inference, speech recognition, speaker verification, etc. DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users.
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Taxonomy
TopicsNatural Language Processing Techniques · Speech Recognition and Synthesis · Topic Modeling
