docs/prior-art-and-plan.md

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+# Prior art and plan
+
+Meta asked (#weksa, 2026-11-13) for scaffolding to stand on instead of
+building from base principles. This is a survey of what exists and a plan that
+borrows it. Every source below was read in full text unless marked **snippet**
+(seen only in a search result) or **not read**. Research only: nothing here is
+built, and no new loop machinery is proposed.
+
+## What exists
+
+**Semantic backbones: none carries the part we care about.**
+
+- AMR ([Banarescu 2013](https://aclanthology.org/W13-2322.pdf)): no politeness,
+  speech-act or pragmatics text at all; it also does not distinguish real from
+  hypothetical events (its Section 4).
+- UMR ([Van Gysel 2021](https://par.nsf.gov/servlets/purl/10288269)): adds
+  document-level coreference, temporal and modal-dependency graphs. The paper
+  has no politeness, addressee or register layer; the only speaker-like object
+  is the author node. Annotation agreement was still "awaiting confirmation".
+- Universal Dependencies: the `Polite` feature has four values (`Infm`, `Form`,
+  `Elev`, `Humb`; [feature page](https://universaldependencies.org/u/feat/Polite.html)),
+  but the Japanese GSD treebank carries no `Polite`
+  ([treebank page](https://universaldependencies.org/treebanks/ja_gsd/index.html)).
+  The page says Japanese *teineigo* shares the value `Form` with formal register
+  although it is a different axis. UD is a parse scheme, not a meaning scheme.
+- Grammatical Framework RGL: 38 languages, Japanese present
+  ([synopsis](https://www.grammaticalframework.org/lib/doc/synopsis/index.html),
+  [status](https://www.grammaticalframework.org/lib/doc/status.html)); the source
+  declares `Style = Plain | Resp`, so two politeness levels and no honorific
+  paradigm
+  ([ResJpn.gf](https://raw.githubusercontent.com/GrammaticalFramework/gf-rgl/master/src/japanese/ResJpn.gf)).
+  LGPL/BSD, application grammars may take any licence
+  ([LICENSE](https://raw.githubusercontent.com/GrammaticalFramework/gf-rgl/master/LICENSE)).
+- Abstract Wikipedia / Wikifunctions: encyclopedic content, 2026 soft launch in
+  three languages, no politeness
+  ([Meta page](https://meta.wikimedia.org/wiki/Abstract_Wikipedia)). The
+  Wikifunctions NLG and Japanese documentation were **not read**.
+
+**Typology.**
+
+- WALS (CC BY 4.0, CLDF at <https://github.com/cldf-datasets/wals>): the only
+  politeness feature is 45A, pronoun politeness
+  ([chapter](https://wals.info/chapter/45)); Japanese is coded value 4, pronoun
+  avoidance. No feature for verbal honorifics or register.
+- Grambank (CC BY 4.0, <https://github.com/grambank/grambank>): GB415 is the
+  second-person politeness distinction; I found no verbal-honorific feature
+  (loose name match, not exhaustive). The 195-feature / 2,467-variety size is
+  **snippet**; Japanese's GB415 value was **not looked up**.
+
+**Formality control in MT: the closest prior art, and it is a sentence-level
+label.**
+
+- [Sennrich 2016](https://aclanthology.org/N16-1005.pdf): a side-constraint
+  token controls T/V; forced-polite output was 96% polite or neutral.
+- [Feely 2019](https://aclanthology.org/D19-5203.pdf): the same for EN-JA with
+  three levels (informal, polite, formal); labels by rule plus a fastText
+  classifier (F1 0.83-0.86 on 150 hand-labelled sentences); formal text is only
+  0.7-1.6% of training data.
+- [CoCoA-MT](https://arxiv.org/pdf/2205.04022) (CDLA-Sharing-1.0, repo
+  <https://github.com/amazon-science/contrastive-controlled-mt>): 1,000 train and
+  600 test segments for Japanese with contrastive formal/informal references and
+  phrase-level markers. Two baseline systems' EN-JA outputs, labelled by
+  professionals, came out formal 29.0% / 73.8% and "other" about 20-24%; EN-JA
+  inter-annotator agreement 0.82, the lowest of the six languages.
+- [Marrese-Taylor 2023](https://aclanthology.org/2023.wmt-1.49.pdf): GPT-3 and
+  ChatGPT prompted reach about 90% formality-control accuracy on EN-JA, which
+  the authors say beats fine-tuned MT; classifier and data at
+  <https://github.com/epochx/japanese-formality> (no licence reported).
+- [IWSLT 2023 formality track](https://aclanthology.org/2023.iwslt-1.1.pdf): no
+  Japanese; metrics matched-accuracy (phrase markers) and classifier-accuracy.
+  Niu 2017 is read ([D17-1299](https://aclanthology.org/D17-1299.pdf)); Niu 2019
+  and Rippeth 2022 (arXiv 2205.06644, abstract only) are **not read**.
+- None of these takes a speaker-state or addressee relation. The label is one
+  bit or three levels attached to a source sentence.
+
+**Grammar-in-context (for conlang targets).**
+
+- [MTOB](https://arxiv.org/pdf/2309.16575) (MIT code, CC BY 4.0 grammar book):
+  Claude 2 with word list plus retrieved sentences plus long grammar 44.7 / 45.8
+  chrF against a human learner at 51.6 / 57.0.
+- [Aycock 2025](https://arxiv.org/pdf/2409.19151): almost all of the gain comes
+  from the book's parallel examples, not its explanations (Gemini-1.5-Flash
+  chrF++ 30.8 with examples only, 22.6 with explanations only, 34.4 whole book).
+- [Gemini 1.5 report](https://arxiv.org/pdf/2403.05530): full book 5.46 against a
+  human learner's 5.52 on a 0-6 human scale.
+  Consequence: a conlang target wants worked paired examples first and grammar
+  prose second.
+
+**Prompt optimisation and evaluation.**
+
+- [DSPy](https://arxiv.org/pdf/2310.03714) (MIT), [MIPRO](https://arxiv.org/pdf/2406.11695),
+  [OPRO](https://arxiv.org/pdf/2309.03409) (Apache-2.0): all assume hundreds of
+  scoring examples (MIPRO's smallest train set is 120; compiles take "10-20
+  trials over 150-300 validation examples"). MIPRO found bootstrapped demos
+  gave the biggest gain and tests with Wilcoxon over 5 runs. OPRO discusses
+  overfitting to the training set.
+- [Koehn 2004](https://aclanthology.org/W04-3250.pdf): paired bootstrap;
+  against a 30,000-sentence reference, a 2.0-point BLEU gap was called right in
+  31% of runs at n=100 and 65% at n=300; a 5.1-point gap 97% at n=100. At
+  n=100 the paper calls the size too small.
+- [Dror 2018](https://aclanthology.org/P18-1128.pdf): paired bootstrap is
+  "less effective for small test sets"; use approximate randomization above 20
+  points; only 3 of 110 surveyed papers corrected for multiple comparisons.
+- [Kocmi 2021](https://arxiv.org/pdf/2107.10821): pairwise accuracy against
+  humans COMET 83.4, BLEURT 80.0, chrF 75.6, BLEU 74.6; recommends COMET
+  primary, a string metric secondary and a paired test; notes chrF fares better
+  than pretrained metrics on logographic targets.
+- [chrF](https://aclanthology.org/W15-3049.pdf): validated on seven targets, Japanese
+  not among them.
+
+## What we borrow
+
+| Need | Borrow | Instead of |
+|---|---|---|
+| Politeness vocabulary for the packet's register field | UD `Polite` values (Infm, Form, Elev, Humb) as the label set, and Feely's three-level Japanese split | inventing level names |
+| Modal/author structure | UMR's conceiver and modal-dependency idea for evidence/stance fields | a bespoke modality scheme |
+| Japanese test of register | CoCoA-MT JA set (600 contrastive test segments, phrase markers) and the Japanese-formality classifier | hand-written register references only |
+| Conlang scaffolding | MTOB recipe: worked paired examples first, word list, grammar prose after | long grammar prompts alone |
+| Typology priors | WALS 45A and Grambank GB415 to seed which targets have a politeness axis | per-language guesses |
+| Significance | paired approximate randomization over items; chrF with an embedding metric | reading small differences as results |
+| Optimiser | DSPy/MIPRO only once a scoring set of 150 or more items exists | the golf loop growing a private optimiser |
+
+We do not adopt AMR, UMR, UD or GF as the packet. None represents the speaker's
+relation to the addressee, which the packet's scene state does. The packet stays
+ours; it borrows vocabulary.
+
+## What is actually new
+
+No source read here conditions politeness on a speaker-addressee relation, a
+speaker's status, restraint or stance. Formality MT takes a label, UMR's modal
+layer takes the author, and GF's `Style` is a parameter. Weksa's claim is the
+step from persona state (status, restraint, stance, relation) to a register
+decision, shown to hold under measurement. CoCoA-MT's finding that over 20% of
+system output is neither formal nor informal says the target is real: a
+prompted model does not hold a register on its own.
+
+Caveat: this absence is relative to the sources I read. I did not search
+sociolinguistics (politeness theory, Brown-Levinson) or persona-conditioned
+dialogue generation, and either may hold closer work.
+
+## Smallest end-to-end slice
+
+One path, existing machinery, ja-JP only, no new loop:
+
+1. A packet with scene state (speaker, addressee, relation) goes through the
+   current `weksa loop` encode and lowering to Japanese.
+2. The same packet under two relations (to a superior and to an intimate) must
+   produce the two UD `Polite` levels; check with the Japanese formality
+   classifier plus a native-reader pass on the 35-item set already in
+   `data/original`.
+3. Score the register decision as a classification (matched accuracy), not only
+   chrF: the CoCoA-MT JA contrastive references give an outside check that the
+   classifier agrees with professional labels.
+
+Out of scope for the slice: conlang targets, optimisers, a second language.
+
+## What good enough means
+
+- Register accuracy: the requested level appears in at least as many
+  outputs as a plain prompted model given the relation in words, by a margin
+  larger than the paired-randomization noise. Marrese-Taylor reports about 90%
+  for prompted LLMs, so that is the bar to clear, not zero.
+- Meaning is not lost: chrF and meaning cosine do not fall below the plain
+  prompt on the same items.
+- Honest sample size: 35 items per language detects only large gaps
+  (Koehn at n=100 misjudges a 2-point gap two times in three), so results are
+  reported with intervals and "not detected" stands as an outcome. Small gaps
+  are not claims.
+- A native-reader check on register for a sample, because every automatic metric
+  here was validated on languages other than the persona register we care about.
+
+## Open
+
+- Which persona fields (status, restraint, stance) are in the packet today
+  versus planned: this doc does not decide that; `docs/interlingua-standard.md`
+  owns it.
+- Licences not checked: the UMR dataset, the other four Japanese UD treebanks,
+  CoCoA-MT repo licence ambiguity (paper says CDLA-Sharing-1.0, GitHub says
+  NOASSERTION).