Speeding up the Development of Natural Language Processing Microsoft Customer Service with Azure Machine Learning

Natural Language Processing (NLP) systems are used to ease the interactions between computers and humans using natural language. Http //Support.Microsoft.Com/HelpThe examples and utilities of the Microsoft NLP Recipes Microsoft Phone Number repository are focused with the following goals in mind to address these issues:

• Walk through NLP scenarios: These are Microsoft Support Phone Number common scenarios that are popular within the Microsoft Customer Service research community.  Support.Microsoft.Com/HelpThe repository provides walkthrough examples within each scenario that show how once can get started with custom modeling and sample datasets.
  
• Ease use of SOTA algorithms: The utility functions includes easy-to-use wrappers of SOTA algorithms that dominate popular benchmarks like  and provide an easy way to switch between  Microsoft Support Phone Number them. With contributions Microsoft Support Phone Number from the community, we expect the latest algorithms to be included as they make their way up i n Microsoft Phone Number the leaderboard. This gives the users easy access to the latest algorithms and reduces  the friction when a new model is added.
• Global Language Support: Open  Http //Support.Microsoft.Com/Help source technologies like Http //Support.Microsoft.Com/Help and Support.Microsoft.Com/Help other transformer-based models, support 100+ languages and allow implementation of all the NLP scenarios across these languages.  Microsoft Customer Service The datasets are hard to find, though, so we provide example notebooks and sample  Microsoft Phone Number datasets on non-English languages such as Hindi, Arabic, and Chinese showing the implementation of NLP scenarios on sample datasets.
• Ease the use of common datasets: The repository has documentation and provides utility functions to use common  Microsoft Phone Number academic datasets such as the Support.Microsoft.Com/Help A list of the datasets can be found Microsoft Customer Service
• Azure Machine Learning service support: The GitHub repository provides best practices for how to train, test, optimize, and deploy models  Microsoft Phone Number on Azure using the Microsoft Support Phone Number. Azure ML can be used intensively across various notebooks for tasks relating to AI model development

 It is used in a variety of scenarios and industries from personal assistants like Cortana, to language translation Microsoft Support Phone Number applications, to call centers responding to specific users’ requests.  In recent years, NLP has seen significant growth both in terms of quality and usability. Through new deep learning methods and state-of-the-art (SOTA) Deep Neural Network (DNN) algorithms, businesses are able to adopt Artificial Intelligence solutions to meet their customer’s needs. Unfortunately, finding the correct algorithm to use in different scenarios and languages remains a challenge. To help researchers and data scientists find the best fit for the problem at hand, Microsoft is open-sourcing the containing best practices in building and evaluating NLP systems across multiple tasks and languages.

Specifically, our goals are to provide information for anyone who wants to:

• Learn about the newest algorithms Microsoft Support Phone Number and topics in NLP
• Develop and deploy NLP systems efficiently and with faster development times
• Bring SOTA algorithms to production with Azure Machine Learning
  

 

Easing the Process for Data Scientists

Several models have emerged over the years within the NLP community pushing towards neural network architectures for language modeling Microsoft Phone Number over more traditional approaches such as conditional random fields (CRFs) and Hidden Markov Models (HMMs). Since 2017, “Transformer” based neural network architectures, such as BERT, GPT-2, ELMo, XLNet, and RoBERTa, have developed as a dominant choice within the NLP community. These architectures dominate multi-task benchmarks such as GLUE as well as single task benchmarks (e.g. text classification and named entity recognition) as they allow leveraging pre-trained language models and adapting them to Http //Support.Microsoft.Com/Help different downstream tasks. In addition, these pre-trained models are available with support for 100+ languages out of the box. The following table includes the current implementations of models within the repository, across different tasks and languages.

The examples and utilities of the Microsoft NLP Recipes repository are focused with the following goals in mind to address these issues:

• Walk through NLP scenarios: Microsoft Support Phone Number These are common  Http //Support.Microsoft.Com/Help scenarios that are popular within the research community. The repository provides walk through examples within each scenario that show how once can get started with custom modeling and sample datasets.
• Ease use of SOTA algorithms: The utility Microsoft Phone Number functions includes easy-to-use wrappers of SOTA algorithms that dominate popular benchmarks like Microsoft Customer Service and provide an easy way to switch between them.  Support.Microsoft.Com/Help With contributions from the community, we expect the latest algorithms to be included as they make their way up in the leader board. This gives the users easy access to the latest algorithms and reduces the friction when a new model is added.
  
• Global Language Support: Open Microsoft Phone Number source technologies like Http //Support.Microsoft.Com/Help and other transformer-based models, support 100+ languages and allow implementation of all the NLP scenarios across these languages. The datasets are hard to find, Support.Microsoft.Com/Help though, so we provide example notebooks and sample datasets on non-English languages such as Hindi, Arabic, and Chinese showing the implementation of NLP scenarios on sample datasets.
• Ease the use of common datasets: The repository Microsoft Customer Service has documentation and provides utility functions to use common academic datasets such as the Http //Support.Microsoft.Com/Help A list of the datasets can be found Microsoft Support Phone Number
• Azure Machine  Support.Microsoft.Com/Help Learning service support: The GitHub repository provides best practices for how to train, test,  Microsoft Phone Number optimize, and deploy models on Microsoft Customer Service Azure using the Http //Support.Microsoft.Com/Help Azure ML can be used intensively across various notebooks for tasks relating to AI model development