Pushing the Limits of Low Resource Morphological Inflection
Recent years have seen exceptional strides in the task of automatic morphological inflection generation. However, for a long tail of languages the necessary resources are hard to come by, and state of the art neural methods that work well under higher resource settings perform poorly in the face of a paucity of data. In response, we propose a battery of improvements that greatly improve performance under such low resource conditions. First, we present a novel two step attention architecture for the inflection decoder. In addition, we investigate the effects of cross lingual transfer from single and multiple languages, as well as monolingual data hallucination. The macro averaged accuracy of our models outperforms the state of the art by 15 percentage points. Also, we identify the crucial factors for success with cross lingual transfer for morphological inflection: typological similarity and a common representation across languages.
EMNLP/IJCNLP