A uto NLU : An On-demand Cloud-based Natural Language Understanding System for Enterprises (2020)

Abstract: With the renaissance of deep learning, neural networks have achieved promising results on many natural language understanding (NLU) tasks. Even though the source codes of many neural network models are publicly available, there is still a large gap from open-sourced models to solving real-world problems in enterprises. Therefore, to fill this gap, we introduce AutoNLU, an on-demand cloud-based system with an easy-to-use interface that covers all common use-cases and steps in developing an NLU model. AutoNLU has supported many product teams within Adobe with different use-cases and datasets, quickly delivering them working models. To demonstrate the effectiveness of AutoNLU, we present two case studies. i) We build a practical NLU model for handling various image-editing requests in Photoshop. ii) We build powerful keyphrase extraction models that achieve state-of-the-art results on two public benchmarks. In both cases, end users only need to write a small amount of code to convert their datasets into a common format used by AutoNLU.

## introduction in recent years, many deep learning methods have achieved impressive results on a wide range of tasks, ranging from question answering @xcite to named entity recognition (ner) @xcite to intent detection and slot filling @xcite . even though the source codes of many models are publicly available, going from an open-sourced implementation of a model for a public dataset to a production-ready model for an inhouse dataset is not a simple task. furthermore, in an enterprise, only few engineers are familiar with deep learning research and frameworks. therefore, to facilitate the development and adoption of deep learning models within adobe, we introduce a new system named autonlu. it is an on-demand cloud-based system that enables multiple users to create and edit datasets and to train and test different state-of-the-art nlu models. autonlu's main principles are: at adobe, autonlu has been used to train nlu models for different product teams, ranging from photoshop to document cloud. to demonstrate the effectiveness of autonlu, we present two case studies. i) we build a practical nlu model for handling various image-editing requests in photoshop. ii) we build powerful keyphrase extraction models that achieve state-of-the-art results on two public benchmarks. in both cases, end users only need to write a small amount of code to convert their datasets into a common format used by autonlu. ## related work closely related branches of work to ours are toolkits and frameworks designed to provide a suite of state-of-the-art nlp models to users @xcite @xcite @xcite . however, several of these works do not have a user-friendly interface. for example, flair @xcite , neuronblocks @xcite , and jiant @xcite require users to work with command-line interfaces. different from these works, an end-user with no programming skill can still create powerful nlu models using our system. furthermore, most previous works are not explicitly designed for enterprise settings where use-cases and business needs can be vastly different from team to team. on the other hand, since autonlu is an on-demand cloud-based system, it provides more flexibility to end users. in 2018, google introduced automl natural language 1 , a platform that enables users to build and deploy machine learning models for various nlp tasks. our system is different from automl in the following aspects. first, automl uses neural architecture search (nas) @xcite to find the best model for the task of interest. as users are not allowed to simply choose an existing architecture, the process can be time-consuming even for simple tasks (e.g., 2∼3 hours). on the other hand, autonlu provides a rich gallery of existing architectures for nlu. in future work, we are also planning to integrate nas into autonlu. second, as a self-hosted solution, autonlu provides product teams of adobe with total control over their datasets and trained models. this enhances privacy and provides more flexibility at the same time. for example, as of writing, there is no way to download a trained model from automl to a local machine to use it for a subsequent task. autonlu supports it out-of-the-box. ## conclusion in this work, we introduce autonlu, an ondemand cloud-based platform that is easy-to-use and has enabled many product teams within adobe to create powerful nlu models. our design principles make it an ideal candidate for enterprises who want to have an nlu system for themselves, with minimal deep learning expertise. autonlu 's code is in the process to be open-sourced, and we invite contributors to contribute. in future work, we will implement more advanced features such as transfer learning, knowledge distillation and neural architecture search, which have been shown to be useful in building real-world nlp systems @xcite @xcite @xcite ). furthermore, we will extend our system to have more advanced analytics features @xcite , and to better support other languages (nguyen and nguyen, 2020).