Segment-based tools chop language into pieces and lose the meaning in between. Don't take our word for it — flip the switch and watch it happen.
{{ switchHint }}
Splitting sentences at arbitrary points forces translators to reason about fragments rather than meaning — producing unnatural phrasing and outright mistakes.
Some in the industry now call this linguistic debt: stale segments and uncorrected errors compounding like unpaid technical debt, degrading every project that draws from the memory.
Translators are made to think in pieces rather than ideas — reducing a nuanced human craft to mechanical, segment-by-segment labour.
String-matched, segment-level reuse compounds its own errors into linguistic debt — every imperfect fragment gets matched and re-served until the output drifts. The memory itself is worth keeping; matching it sentence-by-sentence on surface strings is what decays. Hit the button and watch one sentence go.
AI reads entire documents in one pass, carrying intent and nuance across every paragraph and page.
Knowledge graphs map how concepts relate to each other, grounding every translation in meaning.
A modular, automated system that assembles the best available tools in real time and keeps adapting as the toolset evolves.
Past translations get re-encoded as embeddings, so the system retrieves them by matching meaning — the same shift already shipping in ModernMT and RWS Language Weaver.
Savings reported on high-repetition content — by reusing the memory asset, not discarding it.
Of the document is in view — context-dependent errors like pronouns and terminology resolve across sentences, not within one box.
Domain accuracy vendors report when memory is retrieved as semantic suggestions, not string matches.
Sentence-level reuse was pegged at ~20% of documents — and chunk-level at 80% — by a 2005 vendor claim nobody has tested since. The ceiling is a design choice no one ever measured.
The segment was a scaffold, built for a 1990s bottleneck — when the machine couldn't translate, software helped humans reuse and stay consistent. That bottleneck is gone, and the scaffold has hardened into a cage. The memory you've built still has value; it just stops being a string-matched lock and becomes a semantic asset feeding a model that reads the whole. Move the segment from the core of the workflow to one signal among many — and route the exceptions to people.
Translation Memories: A History — from Arthern's 1978 TERRIER proposal to Trados' 2024 Neural Fragment Recall.
© 2026 Drop the Segments · A position on the future of translation