The language industry has been significantly disrupted by artificial intelligence, with developments appearing thick and fast. Examples of Linguistic AI capabilities in translation technology include:
- Neural machine translation (NMT) with adaptable language pairs: NMT systems trained on existing translation memory and termbase data, with automatic post-editing feedback.
- Content analysis: extracting domain classifications and keywords to help project managers focus on the big picture rather than process management.
- Retrieval-augmented generation (RAG): supplementing LLMs with input from translation memory (TM), terminology databases and NMT for improved translation quality.
- Natural language user interface: using natural language to search and access product documentation, generate reports or analyze projects.
- Automated speech to text: converting spoken language into written text using AI-driven transcription, enabling the translation of audio content within translation workflows.
- Automated post-editing: enhancing the quality of translated content through AI-driven post-editing.
- Automated quality scoring: evaluating and improving translations with automated quality assessment scores.