We describe an experiment in rapid development of a statistical machine translation (SMT) system from scratch, using limited resources: under this heading we include not only training data, but also computing power, linguistic knowledge, programming effort, and absolute time.

A formalism is presented for lexical specification in unification-based grammars which exploits defeasible multiple inheritance to express regularity, sub-regularity, and exceptions in classifying the properties of words. Such systems are in the general case intractable; the present proposal represents an attempt to reduce complexity while retaining sufficient expressive power for the task at hand. Illustrative examples are given of morphological analyses from English and German.

Recent developments in generation algorithms have enabled work in unification-based computational linguistics to approach more closely the ideal of grammars as declarative statements of linguistic facts, neutral between analysis and synthesis. From this perspective, however, the situation is still far from perfect; all known methods of generation impose constraints on the grammars they assume.We briefly consider a number of proposals for generation, outlining their consequences for the form of grammars, and then report on experience arising from the addition of a generator to an existing unification environment. The algorithm in question (based on that of Shieber et al. (1989)), though among the most permissive currently available, excludes certain classes of parsable analyses.