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. 6. Language Technology Implementation

6.2 Creating a Language-Specific Peculiarities (LSP) Document

Previous6.1 Navigating the Language Technology LandscapeNext6.3 Open source data and models

Last updated 1 year ago

Before embarking on the creation of language tools, it is highly recommended to consider the development of a Language-Specific Peculiarities (LSP) document. An LSP document is designed to serve as a comprehensive reference guide that outlines the unique linguistic features of a given language. This document is particularly valuable for projects involving Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) systems, providing essential insights into pronunciation, orthography, and other language-specific nuances.

An LSP document offers a structured overview of various linguistic aspects crucial for phonemic and orthographic transcription. It covers elements like phonemic variation, syllabic structure, suprasegmentals such as stress and intonation, and foreign language influences that shape the language's phonological characteristics.

Orthography and Input Conventions

Understanding the orthographic conventions of a language is essential for accurate transcription. An LSP document delves into orthography by detailing character sets, romanization schemes, punctuation rules, and spelling variations. It also addresses text normalization for elements like numerals, percentages, dates, addresses, acronyms, and abbreviations, ensuring consistency in transcription.

Other Language Items and Function Words

Beyond phonetic and orthographic considerations, an LSP document delves into other linguistic components. This includes comprehensive tables for digits, alphabet pronunciations, month names, weekday names, and common date and time expressions. Additionally, function word lists for pronouns, articles, conjunctions, prepositions, and filler words enrich the document's linguistic insights.

Creating an LSP Document

Creating a robust LSP document involves linguistic expertise. Collaborating with linguists who possess a deep understanding of the language's phonetic, phonological, and orthographic characteristics is vital. The linguist's role is to meticulously analyze the language's features and formulate concise guidelines. This can be achieved through a thorough study of linguistic resources, phonetic and orthographic patterns, and interactions with native speakers. The LSP document serves as a reference for developers and researchers, aiding in the accurate development of language technology applications that align closely with the language's natural nuances.

In essence, an LSP document acts as a linguistic compass, guiding the development of language technology solutions that reflect the authenticity and intricacies of the language. Through collaboration with linguists and rigorous study of phonological and orthographic patterns, this document becomes an invaluable tool, enhancing the accuracy and effectiveness of ASR and TTS projects.

In 2022, Appen collaborated with CLEAR Global to create an LSP document for Sheng, a Swahili and English-based language used by the youth in Nairobi, Kenya. You can find this resource together with a template LSP document to guide your LSP creation process through this page.

LogoResourcesTWB Gamayun Portal