Abstract

In performing morpho-phonological sequence processing tasks, such as letterphoneme conversion or morphological analysis, it is typically not enough to base the output sequence on local decisions that map local-context input windows to single output tokens. We present a global sequence-processing method that repairs inconsistent local decisions. The approach is based on local predictions of overlapping trigrams of output tokens, which open up a space of possible sequences; a data-driven constraint satisfaction inference step then searches for the optimal output sequence. We demonstrate significant improvements in terms of word accuracy on English and Dutch letter-phoneme conversion and morphological segmentation, and we provide qualitative analyses of error types prevented by the constraint satisfaction inference method. 1

Citations