A quick primer: what’s what (and why it matters)
- Disfluency = Audible imperfections in speech: fillers (“uh,” “you know”), false starts, repetitions, elongations, repairs. Humans use them to manage turn-taking, signal uncertainty, soften claims, or buy thinking time. Models often delete or mistranscribe them, losing intent.
- Slang = Informal, in-group vocabulary that evolves fast (“low-key,” “sus,” “peak”). It’s social identity in word form. Polysemy and short half-lives make it easy for models to miss or overgeneralize.
- Idioms = Fixed expressions with nonliteral meanings like “spill the beans,” and “kick the bucket.”. Literal parsing fails; meaning must be retrieved as a unit.
- Subtext = Implied meaning via pragmatics, tone, power, and context. “That’s… ambitious” can be praise, a put-down, or a gentle no. Subtext requires world knowledge and perspective-taking.
Signals, not noise: disfluency carries meaning
A sentence like, “I — I can probably help … later?” encodes hesitation, caution, and weak commitment. If ASR or cleanup filters strip stutters, filler, or rising intonation, downstream models may over-state confidence.
Annotation pattern
- Keep transcripts verbatim, then add a disfluency layer:
FILLER, REPEAT, REPAIR, HEDGE, LENGTHEN, PAUSE_<ms>. - Add function tags (why it’s there): HESITATION, TURN-HOLD, SOFTENING, EMPHASIS.
- In summaries, preserve hedges (“might,” “I think”) unless guidelines explicitly call for normalization—and mark that decision.
Example
“That’s a whole — a whole ’nother can of worms.”
→ REPEAT(whole), REPAIR(another→’nother), function SOFTENING/HUMOR. Summary keeps the cautionary stance (“raises new complications”), not a firm rejection.
Slang and idioms: from in-group code to teachable units
Slang is dynamic and community-bound; idioms are relatively fixed. Both can be opaque.
Common failure modes
- Time drift: yesterday’s “bad” = good; today’s “peak” = excellent (in some communities).
- Sense over-extension: “fire” ≠ literal combustion.
- Idioms parsed literally: “kick the bucket” ≠ exercise.
Annotation pattern
- Build a slang sense sheet per locale/community with examples in the wild, part-of-speech, register, and sentiment polarity.
- Tag idioms as multi-token spans with IDIOM, a literal/nonliteral flag, and canonical gloss.
- Add lifecycle metadata (emerging / mainstream / fading) to guide generation and moderation.
Example
“He went off in that demo.” → SLANG(intense praise, US tech register)
“Let’s table this.” → US = postpone; UK = bring forward for discussion. Tag REGIONAL_SENSE.
Connotation vs. denotation: regional traps to avoid
Words travel; meanings don’t always come along.
- UK “scheme” = legitimate program; US “scheme” = shady plan.
- US “pants” = trousers; UK “pants” = underwear/“rubbish.”
- US “to table” = delay; UK “to table” = put on the agenda.
- AUS “thongs” = flip-flops; US “thongs” = scant underwear.
- SA “now-now/just now” = not “immediately,” but “soon/after a while.”
Annotation pattern
- Attach geo-dialect metadata to prompts and responses (en-GB, en-US, en-AU, etc.).
- Maintain regional sense inventories; when sense ambiguity is detected, prompt disambiguation or switch to neutral wording.
- During evaluation, run contrastive tests: same prompt, different region tags → check for correct sense choice.
Example
In Australian slang, “yeah nah” means “no,” and “nah yeah” means “yes.” The meaning is determined by the final word, with the first word serving to soften the response. “Yeah nah” is a polite way to refuse something, while “nah yeah” is used to agree or affirm something.
Humor and subtext: timing, targets, and face-work
Humor often relies on incongruity, timing, wordplay, or shared knowledge; subtext rides on politeness strategies and power dynamics.
Annotation pattern
- Label humor type (PUN, IRONY, HYPERBOLE, UNDERSTATEMENT, INCONGRUITY) and target (self, brand, third party).
- For subtext, add speech-act (REQUEST, REFUSAL, WARNING), politeness level, stance (APPROVAL, SKEPTICISM), and escalation cues (e.g., frustration rising).
- Use side-by-side ratings for humor “fit” or tone appropriateness rather than binary judgments.
Example
Customer: “Brilliant. Another ‘seamless’ update.” (eye-roll tone)
→ IRONY, stance FRUSTRATION, recommended reply style: low-ego apology + fix steps.
Workflows that make models glitch-aware
- Glitch-aware transcripts: Keep speech “as produced,” then add layers (disfluency, function, emotion). Avoid premature normalization.
- Sense-first lexicons: Maintain living glossaries for slang/idioms with region and register; auto-flag drift for curator review.
- Context-anchored evaluation: Test on noisy prompts (fillers, repairs), regional variants, and idiomatic paraphrases. Track win rate on noisy inputs as a KPI.
- Preference learning for tone: Use side-by-side rankings to teach when to keep a hedge vs. rewrite for clarity, or when humor is acceptable.
- Geo-dialect routing: Condition generation on locale; default to neutral phrasing when confidence is low.
Practical dos and don’ts
- Do treat disfluency as meaning; don’t auto-strip hedges in summaries.
- Do capture regional senses; don’t assume dictionary denotation equals user intent.
- Do use diverse, native annotators; don’t crowdsource niche slang without calibration.
- Do document normalization policy per use case (ASR vs. customer email); don’t mix policies mid-pipeline.
The payoff
Models that understand “messy human talk” de-escalate faster, mirror brand voice more consistently, and avoid costly misreads across regions. Getting there isn’t magic; it’s method: keep the glitches, annotate the why, and train for intent—not just text.
If you’re building for real conversations, our teams can help design glitch-aware guidelines, lexicons, and evaluation sets across 700+ languages and dialects.