Dynamic compilation of weighted context-free grammars

Weighted context-free grammars are a convenient formalism for representing grammatical constructions and their likelihoods in a variety of language-processing applications. In particular, speech understanding applications require appropriate grammars both to constrain speech recognition and to help extract the meaning of utterances. In many of those applications, the actual languages described are regular, but context-free representations are much more concise and easier to create. We describe an efficient algorithm for compiling into weighted finite automata an interesting class of weighted context-free grammars that represent regular languages. The resulting automata can then be combined with other speech recognition components. Our method allows the recognizer to dynamically activate or deactivate grammar rules and substitute a new regular language for some terminal symbols, depending on previously recognized inputs, all without recompilation. We also report experimental results showing the practicality of the approach.