Help a fellow dev on AI-localization?
A software development team is seeking feedback on their AI-based localization pipeline for HR-related content. They have implemented a methodology using GPT-5-nano for translation and have encountered issues with certain translations that, despite high similarity scores, were flagged by human reviewers. The team is looking for suggestions on improving translation accuracy and understanding metrics that correlate with human acceptance in UI localization.
- ▪The team built an AI-based localization pipeline for their HR software product.
- ▪During a recent Spanish localization run, approximately 75% of strings passed automatically but were later flagged by human translators for inaccuracies.
- ▪The team is seeking insights on improving translation accuracy and metrics for human acceptance in UI localization.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,004 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Ycombinator |
| Canonical URL | https://news.ycombinator.com/item?id=47943730 |
| Publication time | Wed, 29 Apr 2026 03:01:14 +0000 |
| Retrieval time | 2026-04-29T03:41:52.871Z |
| Last seen | 2026-04-29T03:41:52.871Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | fY-WM_yf0-nv |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
We built an AI-based localization pipeline for our software product (HR domain) and would love feedback/ suggestions from others working in production MT/localization, so that we can learn and improve.Current methodology:GPT-5-nano forward translation + back-translationtext-embedding-3-small cosine similarity on source vs. back-translated text.Threshold: ≥0.92 = auto-approvedOn a recent ~970-string Spanish localization run:~75% of strings passed automaticallyWe then had two human translators review outputs, and both flagged several problematic cases:"Add Attachment" → Agregar AdjuntoBetter: Adjuntar Archivo"Pay Grades" → Grados de PagoBetter: Escalas salariales"Sub Unit" → SubunidadBetter: DepartamentoAll three examples still scored 0.94+ cosine similarity.Google Translate also…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.