README.md

metadata

license: apache-2.0
language:
  - ko
  - en
  - zh
  - ja
  - vi

Semantic Router Instruction Dataset — Multilingual

A multilingual instruction dataset for training and evaluating the semantic-turn router: a model that classifies one utterance along 14 axes simultaneously. A single JSONL file of five languages × 600,000 rows = 3,000,000 rows total. Every row's instruction actually contains the content signal its axis/value label depends on.

• Scale: 600,000 rows per language (14 axes × 150 values fully covered, fixed per-value counts — identical in every language)
• Languages: Korean · English · Chinese · Japanese · Vietnamese
• Parallelism: label-parallel, not text-parallel — each language block is authored natively in that language; no row is a translation of another language's row. Translation-parallel training data invites the router to key on translation artifacts, so it is deliberately excluded.
• Character: every row is a long-form composed turn (context + request + constraints/branches). en/zh/ja/vi are built with six distinct structural authoring engines so the corpus is not a single assembly pattern.

Dataset overview

Item	Value
Total rows	3,000,000 (exactly 600,000 × 5 languages)
Size	1,027,674,919 bytes (≈ 0.96 GiB), uncompressed
Encoding	UTF-8, one JSON object per line, no header
Block order	ko → en → zh → ja → vi (contiguous per-language blocks, no shuffle)

Block boundaries: rows 1–600,000 = ko, 600,001–1,200,000 = en, 1,200,001–1,800,000 = zh, 1,800,001–2,400,000 = ja, 2,400,001–3,000,000 = vi. Row order inside each block is fixed and reproduces identically on rebuild.

Schema

Each row is a JSON object with four fields (lang always first):

{"lang":"en","instruction":"I'm putting together the quarterly ops review and my notes are a mess, so pull the numbers from the attached workbook and build the review deck. Keep it under ten slides, lead with the churn story, and if the March figures are missing, use February and say on the slide.","category":"service","value":"presentation"}

Field	Type	Description
lang	string	One of ko / en / zh / ja / vi
instruction	string	User utterance. Unique within each language block (all 600k verified)
category	string	One of the 14 axes
value	string	One of that axis's values (150 values total)

Label strings (category, value) are English snake_case in all blocks, including the non-English ones — they are the router's target vocabulary and are deliberately kept language-independent.

Axes, values, allocation

The table below applies identically to every language block (file-wide, each axis's total is 5× the table figure — e.g. service is 96,000 rows per language → 480,000 rows in the whole file):

Axis	Values	Rows (per language)	Values
service	16	96,000	general, presentation, image, data_analysis, place_search, web_search, knowledge_search, calendar, memo, works, connector, artifact, coding, translation, skill, reasoning
presentationSkillId	56	112,000	executive-strategy, board-update, quarterly-business-review, sales-proposal, investor-pitch, training-explainer, technical-architecture, research-report, marketing-campaign, product-launch, product-roadmap, consulting-diagnosis, all-hands-update, thesis-research, crisis-communication, demo-day-pitch, compliance-training, lesson-lecture, customer-qbr, analyst-briefing, okr-review, esg-report, incident-postmortem, thesis-defense, grant-proposal, workshop-facilitation, competitive-analysis, investor-update, annual-report, pricing-proposal, analytics-readout, customer-onboarding, vendor-selection, market-entry, company-onboarding, case-study-analysis, model-un-position, lab-meeting, self-review, conference-talk, resume-portfolio, nonprofit-pitch, policy-briefing, earnings-brief, m-a-proposal, org-redesign, regulatory-response, course-syllabus, exam-prep, hobby-explainer, travel-itinerary, deck-redesign, deck-exec-polish, deck-board-upgrade, deck-content-cleanup, none
contentDomain	15	60,000	social, play, self_capability, affective_state, creative, support, explore, personal_scope, opinion, code_work, formal_reasoning, supplied_material, explicit_lookup, multi_step_research, unresolved
action	14	56,000	respond, create, revise, import, analyze, search, export, delete, translate, summarize, explain, compare, execute, none
dialogueAct	10	40,000	clarification_answer, option_selection, confirm, decline, correction, continue_output, preference_directive, meta_conversation, undo_action, none
sourcePolicies	7	42,000	web, internal, memory, attachments, conversation_only, auto, none
memoryIntent	7	28,000	informational, transactional, conversational, analytical, personal, navigational, none
requestedOutputs	5	34,000	chat, docx, xlsx, pptx, image
diagramKind	4	24,000	structure, visualization, plan, none
presentationMode	4	24,000	fast, professional, creative, none
complexity	4	32,000	simple, standard, complex, hard
artifactKind	3	18,000	diagram, interactive, none
analysisMode	3	18,000	focused, comprehensive, none
artifactComplexity	2	16,000	simple, complex

Per-value counts are the row count divided by the value count (e.g. 6,000 per service value, 2,000 per presentationSkillId value — 30,000 / 10,000 respectively file-wide). Each instruction is labeled with exactly one (category, value) pair — rows exist per axis, not as multi-label records. Because per-value counts are identical across languages, any language block you slice has the same label distribution (class priors) — this is what makes cross-lingual evaluation compare like with like.

Cross-lingual design

• Label-parallel, not text-parallel. The five blocks align on labels only. No instruction is a translation of another block's instruction.
• Same class priors everywhere. Identical per-value counts mean every per-language slice has the same label distribution.
• Native authorship. Each language block is written from scratch in that language: its own slot pools (topics, companies, cities, apps, document and file names, people, math/logic prompts), its own group mains, and its own engine banks (openers, scene sentences, constraints, email frames, conversational fillers, closers). Real-world anchors are localized — Chinese cities and platforms for zh, Vietnamese names and places for vi, and so on — the way native speakers actually write.
• Technical terms stay in their original script where that is the natural usage (React, Kubernetes, x²−5x+6=0, ECONNREFUSED) — matching how each language's technical community writes.

Authoring architecture

The Korean block composes every row in a single shape: situational opener (+background) + group request + constraint tails. The en/zh/ja/vi blocks route each row through one of six structural authoring engines:

Engine	Shape
composed	Situational opener (95%) + optional background (38%) + main request + 1–4 constraint tails; the last tail may fork conditionally (~35%)
brief	2–3 flowing scene sentences + connector (55%) with the request woven in + optional softener (50%) — no bolted-on opener/tail skeleton
numbered	Lead-in + main request + enumerated constraints (1) …; 2) …; 3) …, 2–4 items)
convo	Conversational filler + request + self-correction (30%) + softener (70%) — dialogue-register turns
email	Inline email framing: greeting + context + ask + request + tail (60%) + closer
forked	Optional opener (60%) + request + 1–2 explicit if/then branch tails + an "either way" base line

Rows are routed first to one of six profiles (by axis/value); each profile fixes the engine mix and the register (work / casual / terse):

Profile	Applied to	Engine mix (weights)
general	most service, sourcePolicies, diagramKind, artifactKind, presentationMode, requestedOutputs, …	composed 30, brief 22, numbered 16, email 12, forked 12, convo 8
formal	complexity::hard, contentDomain::formal_reasoning	brief 28, numbered 24, composed 20, email 14, forked 14
code	contentDomain::code_work, service::coding	numbered 32, composed 22, brief 18, forked 16, email 6, convo 6
quick	complexity::simple	composed 42, brief 30, convo 20, numbered 8
personal	contentDomain::{social,support,opinion,explore,personal_scope}, memoryIntent::personal	convo 36, brief 26, composed 22, email 6, forked 6, numbered 4
micro	dialogueAct (all), memoryIntent::{conversational,navigational,none}, contentDomain::{play,self_capability,affective_state,unresolved}, action::none	convo 72, composed 20, brief 8

The micro profile keeps short dialogue turns ("맞아", "嗯嗯", "そうだね"-style utterances) intact while padding them with surrounding context clauses, so the label's meaning survives inside a long-form distribution.

Per-language contracts

The engine is language-agnostic; every string and orthographic rule lives in a language pack (dataset/generator/i18n/{en,zh,ja,vi}-*.mjs):

Lang	Joining	Terminal	Enumerated list	Per-language notes
en	space-joined	.	1) + ;	email engine lowercases the joined ask; non-Latin leak scan with a math-glyph allowlist (√ ≡ → ∞ — …)
zh	no inter-word spaces	。	1） + ；	fullwidth punctuation, 「」 quotation marks, spacing-normalizing post-pass, doubled-CJK-run scan
ja	no inter-word spaces	。	1） + ；	same CJK contract as zh; polite/plain register mix
vi	space-joined	.	1) + ;	full diacritics; doubled-word scan covers À-ỹ

Quality gates and integrity

Data generation (when each language's 600,000 rows are produced):

• Deterministic: mulberry32 seeded with FNV-1a over `${lang}::${category}::${value}` plus per-pass offsets; no Math.random, no timestamps. Identical inputs rebuild byte-identical output. The final shuffle uses a fixed global seed plus a per-language offset. Writes are atomic (.tmp + rename).
• Generation: 14 passes per group, ~1.2× attempts, local + global dedupe across all 150 groups — each language's 600,000 instructions are unique.
• Build-time validation (a build is OK only when all of these hold): exactly 600,000 lines; every line valid JSON with exactly instruction/category/value; zero unresolved {slot} leaks; 100% unique instructions; and the language-specific scans at zero — doubled-word rows for en/vi, doubled 2-char CJK runs for zh/ja, non-Latin leaks for en.

File integrity — dataset.all.jsonl only exists if every gate below passes (generator/merge.mjs enforces them at write time; on failure no file is left behind):

1. Fixed language order: ko → en → zh → ja → vi contiguous blocks
2. Every row JSON-parsed + 4 field type checks + lang code consistency
3. Exactly 600,000 rows per language · 3,000,000 total
4. Written to .tmp and renamed only on success (atomic) — a partial file that failed validation cannot exist

Length profile

All five blocks share the long-form design goal, but absolute character counts are script-dependent (a Han character carries far more than a Latin letter) — compare within a script, not across:

Lang	Avg chars	≥80	≥120	Min	Max
ko	140.2	92.8%	69.5%	25	273
en	204.4	95.0%	85.6%	17	483
zh	72.8	39.4%	3.0%	5	165
ja	98.4	73.1%	26.6%	6	226
vi	205.9	94.9%	86.7%	12	470

Caveats

• This is template-combination generated data. Individual sentences are grammatical, but rare semantic mismatches between slot combinations (a work topic meeting a casual opener, etc.) can occur. Labels are unaffected; account for it in naturalness evaluations.
• The same topic may appear under different axes, and near-identical strings may exist across different axes within one language block. Instructions are unique within each language block (verified exhaustively); blocks are not deduped against each other (by design).
• Cultural/regional anchors differ per language on purpose. The invariant is the label, not the surface content.