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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4. Identifying Impactful Use Cases

Chapter 4 Overview:

Chapter overview:

In this chapter, we look at the way organizations select language technology and how they can use it effectively. We compare the process to choosing the right tool for the job. We also explain the importance of making sure the use of technology fits with the organization’s goals, the workability of the idea, and the overall benefits.

This chapter also helps organizations to work through the process of finding use cases for language technology that will have a strong impact. The focus is on the needs of users, workability, and continuous improvement. We offer practical steps so that organizations can make sure the technology they introduce fits in with their mission and goals.

When we bring language technology into our everyday lives, it’s important to make smart choices about where and how to use it. Think of it as picking the perfect tool for the job. Our success in using language technology will depend on how well it fits with what we want to achieve, how workable it is to use, and what benefits it brings to the organization.

Use Table 2 from this partner playbook to get a summary of the sections in Chapter 4.

Section

Content

4.1 – Setting criteria to help choose the use case

We show organizations how to set clear objectives so that they can use language technology effectively. They need to identify the challenges that language technology can overcome. They also need to understand their technical capabilities and evaluate the impact the technology might have. And they need to make sure it meets the needs of stakeholders and can be scaled up.

4.2 – Carrying out a needs assessment

It is very important to do a needs assessment using the principles of human-centered design (HCD).

To do this, you need to:

  • identify stakeholders and end users

  • collect existing information

  • build empathy through interviews or surveys

  • create user personas

  • map user journeys

  • define and prioritize problems

  • validate findings and

  • share results.

This process forms the basis for solutions that will have an impact. This is because they reflect an in-depth understanding of user needs.

4.3 – Evaluating what can be done and what works

To check whether the project is workable, the playbook suggests small-scale proof of concepts (POC) to test language technology solutions. You will need to define key performance indicators (KPIs) to measure success, and carry out a cost-benefit analysis. You also need to identify risks and develop strategies to deal with them. The chapter suggests a step-by-step approach so you can keep checking your progress and making improvements. It’s important that you can adapt if there are changes in needs or in technology.

Table 2: Section summary for Chapter 4

This section of the playbook requires little or no technical expertise and has been designed to be plain and concise with a focus on introducing language technology and how it can be integrated into programs, for organizations looking to use language AI for community engagement.

When it comes to bringing language technology into our everyday lives, making smart choices about where and how to use it is important. Think of it as picking the perfect tool for a job. The success of using language technology depends on how well it fits with what we want to achieve, how feasible it is to use, and how beneficial it can be to the organization.

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