Linguistic Data Sources That Power AI-Assisted Translation Workflows

1.Translation Memories (TMs)

  Bilingual segments (source + target pairs) stored from past translations.
Include metadata such as project name, client, domain, date, and editor.
Essential for maintaining consistency in repetitive or versioned content.

2. Bilingual/Multilingual Reference Files

Final bilingual files from past projects, such as:

  XLIFF, TMX, PO, or Excel tables

  Subtitled files (e.g., SRT, ASS) with source and target

  Bilingual DOCX with side-by-side columns

Ideal for building context-aware reference frameworks.

3. Glossaries / Termbases

  Terminology lists that include:

  Preferred translations

  Definitions, usage notes, context examples

  Part of speech

These can be monolingual, bilingual, or multilingual, often tailored to industry or client-specific usage.

4. Style Guides

  Documents detailing:

  Brand tone and voice

  Regional spelling (e.g., UK vs. US), punctuation, and formatting preferences

  Do/don’t examples

Can be global or specific to a single client or domain.

5. Project Instructions / Linguistic Guidelines

Include:

  Task briefs

  Internal reviewer notes

  Cultural or linguistic adaptation expectations

Usually shared as PDFs or Word documents—crucial for helping AI interpret beyond literal meaning.

6. Client Feedback Reports / QA Results

  Contain LQA data, post-editing feedback, and categorized errors

Useful for "learning from corrections"

Can follow structured models (e.g., MQM, DQF) or be informal

7. Monolingual Reference Corpora

  Collections of well-written, domain-relevant texts in the target language:

  Technical manuals

  Marketing brochures

  Legal documents

These improve fluency, idiomatic usage, and genre sensitivity.

8. Publicly Available Knowledge Sources

Useful for general language enhancement (when permitted):

  Wikipedia

  EU Parliament proceedings

  IATE, UN termbases

Caution: Must be filtered to avoid bias or mismatched context in domain-specific translation.

Optional but Powerful Sources

These aren't always prioritized but offer great value in refining AI translation outputs:

  Internal Wikis / Knowledge Bases – For product-specific terms and usage.

  FAQs, Chat Logs, Support Emails – Ideal for conversational tone training.

  Marketing Collateral / Product Descriptions – Improve brand alignment and persuasive style.

By carefully curating and feeding these resources into your AI workflow—whether via prompt engineering or RAG—you can dramatically increase translation relevance, reduce hallucinations, and ensure high-quality output that matches professional standards.