Computational Linguistics Commons^™

Phonologically Informed Edit Distance Algorithms For Word Alignment With Low-Resource Languages, Richard T. Mccoy, Robert Frank

Robert Frank

We present three methods for weighting edit distance algorithms based on linguistic information. These methods base their penalties on (i) phonological features, (ii) distributional character embeddings, or (iii) differences between cognate words. We also introduce a novel method for evaluating edit distance through the task of low-resource word alignment by using edit-distance neighbors in a high-resource pivot language to inform alignments from the low-resource language. At this task, the cognate-based scheme outperforms our other methods and the Levenshtein edit distance baseline, showing that NLP applications can benefit from information about cross-linguistic phonological patterns.