Language AI Playbook
  • 1. Introduction
    • 1.1 How to use the partner playbook
    • 1.2 Chapter overviews
    • 1.3 Acknowledgements
  • 2. Overview of Language Technology
    • 2.1 Definition and uses of language technology
    • 2.2 How language technology helps with communication
    • 2.3 Areas where language technology can be used
    • 2.4 Key terminology and concepts
  • 3. Partner Opportunities
    • 3.1 Enabling Organizations with Language Technology
    • 3.2 Bridging the Technical Gap
    • 3.3 Dealing with language technology providers
  • 4. Identifying Impactful Use Cases
    • 4.1 Setting criteria to help choose the use case
    • 4.2 Conducting A Needs Assessment
    • 4.3 Evaluating What Can Be Done and What Works
  • 5 Communication and working together
    • 5.1 Communicating with Communities
    • 5.2 Communicating and working well with partners
  • 6. Language Technology Implementation
    • 6.1 Navigating the Language Technology Landscape
    • 6.2 Creating a Language-Specific Peculiarities (LSP) Document
    • 6.3 Open source data and models
    • 6.4 Assessing data and model maturity
      • 6.4.1 Assessing NLP Data Maturity
      • 6.4.2 Assessing NLP Model Maturity:
    • 6.5 Key Metrics for Evaluating Language Solutions
  • 7 Development and Deployment Guidelines
    • 7.1 Serving models through an API
    • 7.2 Machine translation
      • 7.2.1 Building your own MT models
      • 7.2.2 Deploying your own scalable Machine Translation API
      • 7.2.3 Evaluation and continuous improvement of machine translation
    • 7.3 Chatbots
      • 7.3.1 Overview of chatbot technologies and RASA framework
      • 7.3.2 Building data for a climate change resilience chatbot
      • 7.3.3 How to obtain multilinguality
      • 7.3.4 Components of a chatbot in deployment
      • 7.3.5 Deploying a RASA chatbot
      • 7.3.6 Channel integrations
        • 7.3.6.1 Facebook Messenger
        • 7.3.6.2 WhatsApp
        • 7.3.6.3 Telegram
      • 7.3.7 How to create effective NLU training data
      • 7.3.8 Evaluation and continuous improvement of chatbots
  • 8 Sources and further bibliography
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  1. 4. Identifying Impactful Use Cases

4.1 Setting criteria to help choose the use case

If your organization wants to start using language technology in your work, there are several criteria you can use to choose a use case for language technology. The first step is to outline the goals you hope to achieve by using language technology. Setting goals

You can do this by identifying the challenges that language technology can help you with. For example:

  • improving customer service

  • boosting productivity

  • improving communication or

  • making a service more accessible to marginalized groups and communities.

Technical capacity

The next step is to understand your technical capacity. Is there a gap between what you want to do and your technical skills? If you identify this technical gap, it will make it easier when you are working with technology partners. Potential impact

It’s important to look at the impact language technology could have. This will help you decide if a use case is worth following up.

You can ask questions like:

  • “Will this improve the daily lives of its users?”

  • “Will more people have access to our service?”

  • “Will this make internal or external teams more efficient?” or

  • “Will it cut the cost of achieving our goals?”

Meeting the needs of stakeholders and scalability

Another way of choosing use cases with an impact is by simply talking to stakeholders. You need to understand their pain points and find out what they expect when it comes to communication. This will also help you to understand whether the use case can be scaled up. Can the use case be expanded? Can it be used in other areas or projects within the organization?

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Last updated 1 year ago