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. 3. Partner Opportunities

3.3 Dealing with language technology providers

Once a use case is developed, it will be easier to identify and select an appropriate technology provider. The technical development work can then be carried out by partners who specialize in the field and have a good understanding of the processes around building or deploying digital solutions, including language technology.

Areas that are useful to explore, define, and consider when choosing a technical partner:

  • Relevant expertise: Assess the partner’s expertise in language technology. Look for partners with experience and knowledge that directly relate to the objectives of your project.

  • Collaborative attitude: Choose partners who are willing to engage in a collaborative process. A partner who is open to sharing feedback, participating in discussions, and contributing insights will enhance the delivery process.

  • Resource availability: Consider the partner’s availability and resources to actively participate in the process. Ensure they have the time and the right personnel assigned to work effectively.

  • Similar values and goals: Select partners whose organizational values and goals align with yours. This alignment will create a more cohesive partnership and increase the likelihood of a successful collaboration.

  • Past collaborations: If possible, review the partner’s history of collaborations. This can provide insights into their approach and commitment.

  • Communication skills: Effective communication is crucial for a successful tech delivery process. Choose partners who are articulate, responsive, and capable of clear and constructive communication.

  • Geographic considerations: Depending on the nature of your project and process, consider whether geographic location matters. Local partners might offer more direct engagement opportunities.

  • Size and scale: Consider the size and scale of the partner’s organization. Larger organizations might offer a broader range of perspectives and resources, while smaller organizations could provide more focused, personalized feedback.

  • Network and reach: Partners with a wide network or reach can help amplify the impact of the project by also involving a larger community of stakeholders, such as linguists or communities of speakers of marginalized languages.

Many types of partners can be considered, including non-profit organizations or social enterprises that have tech for social good as their main focus. There are also opportunities to reach out to commercial for-profit organizations that may do in-kind work, although it is still advised to formalize the collaboration to establish responsibilities, processes, communication, and accountability, as well as continuity.

Building teams for language tech projects For a language technology project to be successful, we suggest that the owner of the use case forms a team. This could even be a small volunteer group. You should have at least one focal point in this team who represents the needs of the organization and the users. Doing this will make sure that everyone understands their needs and stays in contact with the target audience. It should also ensure that the project is completed on time and as agreed.

When you are developing language technology solutions, it is very important to work together closely. Building a community to work together means you can develop your expertise together. It also makes sure that the solution design fits to the specific domain, languages spoken, access to technology, and data factors. This is especially true in small volunteer teams. Working together in this way will make your project more effective. It also means people have support and can learn and grow within the team.

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Last updated 1 year ago