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
Powered by GitBook
On this page
  1. 2. Overview of Language Technology

2.2 How language technology helps with communication

Language technology has completely changed the way we communicate with communities. Our communication is now more program-focused and efficient. There are many ways we can use language technology when communicating with communities. It can make the programs much more effective.

Firstly, language technology means we can automate translation and interpreting services. In multilingual settings, effective communication can be difficult. Language technology tools can help organizations to bridge gaps in language and/or literacy. For example, machine translation and speech recognition. We can make sure people understand our messages and we can reach communities with several languages. This means more people are included and can take part in programs.

Language technology also allows real-time communication with communities. By using instant messaging platforms and chatbots, organizations can respond to queries or concerns from community members immediately. People feel listened to and valued. They are more likely to trust the organization and get involved in the program.

Language technology also helps us to collect and analyze data. Organizations can use natural language processing algorithms to find things out from all the text and voice data they collect from surveys or feedback forms. This means they can identify the needs and preferences of a community. They can then make changes to the program in line with these.

A further plus point is that language technology makes it easier to evaluate programs. Automated sentiment analysis tools allow organizations to get an accurate idea of public opinion about their programs, general opinions, and current events. Organizations can also use these tools to analyze social media posts or online reviews. This allows them to find out how effective their programs are. They can make any improvements needed and get an understanding of how communities feel about the program. This information is helpful for future program design.

To sum up, language technology can be very helpful as it provides information and makes two-way communication with communities easier. From translation services to real-time communication and the ability to analyze data, these new developments can help us to scale up community communication. We continue to welcome such technological innovations in our society today. But we must also use these tools to improve understanding between diverse communities so that our programs can be more effective.

Previous2.1 Definition and uses of language technologyNext2.3 Areas where language technology can be used

Last updated 1 year ago