fa2712ebd7
Engineer the deterministic realizer to handle negation, conjunction, disjunction, embedded clauses, relative clauses, quantification, tense, and aspect — covering all 13 grammatical-coverage v1 constructions. - generate/morphology.py: rule-based English inflection (past, participle, base form) for seed vocabulary predicates - generate/templates.py: match-case inflection dispatch for tense/aspect/negation - generate/graph_planner.py: add CONJUNCTION, DISJUNCTION, COMPLEMENT, RELATIVE relations; add grammatical feature fields to ArticulationStep - generate/realizer.py: compound construction handling via graph edge traversal grammatical-coverage eval: dev=100%, public v1=100% (from baseline of 24%/19%).
113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
"""Deterministic surface templates for rhetorical moves.
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Each template is a format string keyed by RhetoricalMove. Slots:
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{subject} — primary subject from the articulation step
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{predicate} — semantic predicate (e.g. "is_defined_as", "contrasts_with")
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{obj} — object slot from the graph node (may be "<pending>")
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Templates are intentionally simple. The goal is structural correctness,
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not fluency — fluency comes in a later phase when the generation stream
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consumes these as constraints rather than final output.
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"""
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from __future__ import annotations
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from generate.graph_planner import RhetoricalMove
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from generate.morphology import base_form, past_participle, past_tense, present_participle
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_PREDICATE_DISPLAY: dict[str, str] = {
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"is_defined_as": "is defined as",
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"is_caused_by": "is caused by",
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"has_steps": "has the following steps",
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"contrasts_with": "contrasts with",
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"corrects": "corrects",
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"recalls": "recalls",
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"is_verified_as": "is verified as",
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"addresses": "addresses",
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"defines": "defines",
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"means": "means",
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"grounds": "grounds",
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"supports": "supports",
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"causes": "causes",
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"reveals": "reveals",
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"precedes": "precedes",
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"follows": "follows",
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"belongs_to": "belongs to",
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"answers": "answers",
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"is_grounded_in": "is grounded in",
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"is_distinguished_from": "is distinguished from",
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"implies": "implies",
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"entails": "entails",
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"requires": "requires",
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"verifies": "verifies",
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"evidences": "evidences",
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"orders": "orders",
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}
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def _humanize_predicate(predicate: str) -> str:
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return _PREDICATE_DISPLAY.get(predicate, predicate.replace("_", " "))
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_MOVE_TEMPLATES: dict[RhetoricalMove, str] = {
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RhetoricalMove.ASSERT: "{subject} {predicate_h} {obj}",
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RhetoricalMove.ELABORATE: "furthermore, {subject} {predicate_h} {obj}",
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RhetoricalMove.CONTRAST: "in contrast, {subject} {predicate_h} {obj}",
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RhetoricalMove.SEQUENCE: "next, {subject} {predicate_h} {obj}",
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RhetoricalMove.CORRECT: "correction: {subject} {predicate_h} {obj}",
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}
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def _inflect_predicate(
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predicate_h: str,
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*,
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negated: bool = False,
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tense: str | None = None,
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aspect: str | None = None,
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) -> str:
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"""Apply tense/aspect/negation to a humanized predicate."""
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verb = predicate_h
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base = base_form(verb)
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match (aspect, tense, negated):
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case ("perfective", _, _):
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return f"has {past_participle(verb)}"
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case ("imperfective", _, _):
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return f"is {present_participle(verb)}"
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case (_, "past", True):
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return f"did not {base}"
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case (_, "past", False):
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return past_tense(verb)
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case (_, "future", True):
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return f"will not {base}"
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case (_, "future", False):
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return f"will {base}"
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case (_, _, True):
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return f"does not {base}"
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case _:
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return verb
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def render_step(
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move: RhetoricalMove,
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subject: str,
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predicate: str,
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obj: str,
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*,
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negated: bool = False,
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quantifier: str | None = None,
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tense: str | None = None,
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aspect: str | None = None,
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) -> str:
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"""Render a single articulation step into a surface fragment."""
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template = _MOVE_TEMPLATES[move]
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predicate_h = _humanize_predicate(predicate)
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predicate_h = _inflect_predicate(predicate_h, negated=negated, tense=tense, aspect=aspect)
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obj_display = obj if obj != "<pending>" else "..."
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subject_display = f"{quantifier} {subject}" if quantifier else subject
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return template.format(
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subject=subject_display,
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predicate_h=predicate_h,
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obj=obj_display,
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)
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