Cross-lingual morphological inflection with explicit alignment

Abstract

This paper describes two related systems for cross-lingual morphological inflection for SIGMORPHON 2019 Shared Task participation. Both sets of results submitted to the shared task for evaluation are obtained using a simple approach of predicting transducer actions based on initial alignments on the training set, where cross-lingual transfer is limited to only using the high-resource language data as additional training set. The performance of the system does not reach the performance of the top two systems in the competition. However, we show that results can be improved with further tuning. We also present further analyses showing that the cross-lingual gain is rather modest.