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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2. Overview of Language Technology

Chapter 2 Overview: In this chapter, we give you an overview of language technology. Language technology focuses on making systems do useful tasks with human language, whether spoken or written. This chapter highlights the huge impact language technology can have on communication, automation, and interaction.

We look at the benefits of using language technology in communication. We also look at its role in automating translation and interpreting services. It enables real-time communication with communities and makes it easier to collect and analyze data, and evaluate programs. The chapter focuses on the potential for language technology to bridge language gaps, increase inclusion, and get more people to participate in programs.

The section on potential uses makes it clear how important language technology can be in disaster response, education, healthcare, and environmental conservation. We look at the way language technology can help in these areas:

  • supporting humanitarian aid

  • improving access to educational materials

  • making healthcare communications more effective and

  • helping the environment by passing on messages about climate change.

We present the key terminology and concepts used in language technology, explaining terms like Artificial Intelligence (AI), Automatic Speech Recognition (ASR), Text-to-Speech (TTS), Machine Translation (MT), and chatbot. Understanding these key terms will help readers to make better use of the playbook. To sum up, Chapter 2 is like a foundation. It gives you the basic knowledge you need to understand the importance of language technology. It can help you deal with communication challenges, improve inclusion, and make programs more effective in many different sectors.

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.

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Last updated 10 months ago

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