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It is not the case that Textual entailment systems that operate on syntactic trees without intensional semantics omit the modal and temporal operators central to Montague Grammar.
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Reasons For
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Reason for
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1.
Most textual entailment datasets lack modal/temporal complexity; syntactic systems achieve high performance on actual tasks without intensional semantics.
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2.
Neural models learn implicit modal/temporal representations from data without formal Montague structures, suggesting explicit intensional semantics unnecessary.
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3.
Montague Grammar's mathematical elegance doesn't entail that practical NLP must implement it; engineering solutions can succeed via different architectures.
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Reasons Against
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Reason against
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1.
Montague Grammar systematically represents modality and temporality; syntactic trees alone cannot distinguish 'must happen' from 'happens'.
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2.
Entailment validity depends on scope relations between operators; syntax without intensional semantics conflates different logical structures.
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3.
Natural language reasoning about obligations, possibilities, and time-order requires explicit modal/temporal operators to avoid false entailments.
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