Morphological reinflection with conditional random fields and unsupervised features

Abstract

This paper describes our participation in the SIGMORPHON 2016 shared task on morphological reinflection. In the task, we use a linear-chain conditional random field model to learn to map sequences of input characters to sequences of output characters and focus on developing features that are useful for predicting inflectional behavior. Since the training data in the task is limited, we also generalize the training data by extracting, in an unsupervised fashion, the types of consonant-vowel sequences that trigger inflectional behavior, and by extending the available training data through inference of unlabeled morphosyntactic descriptions.