Ambiguity-preserving Generation with LFG- and PATR-style Grammars

The widespread ambiguity of natural language presents a particular challenge for machine translation. The translation of an ambiguous source sentence may depend on first determining which reading of the sentence is contextually appropriate and then producing a target sentence that accurately expresses that reading. This may be difficult or even impossible to accomplish when resolution of the source ambiguity depends on a complete understanding of the text, or when several readings are contextually appropriate. An attractive alternative strategy is to circumvent the need for disambiguation by generating a target sentence that has exactly the same ambiguities as the source. In this brief note we investigate whether ambiguity-preserving generation is possible when syntactic structures are described by the mechanisms of LFGor PATR-style grammars (Kaplan and Bresnan 1982, Shieber et al. 1983). Mechanisms of this sort associate attribute-value structures with trees derived in accordance with a context-free grammar. Our result also applies to other systems such as HPSG (Pollard and Sag 1994) whose formal devices are powerful enough to simulate, albeit indirectly, the effect of context-free derivation. Consider as an example the well-known ambiguous sentence (1)