A system submitted (team name: UTNII) to the CoNLL-SIGMORPHON-2017 Shared Task: Universal Morphological Reinflection.
Hajime Senuma and Akiko Aizawa. 2017. Seq2seq for Morphological Reinflection: When Deep Learning Fails. In Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection, pages 100–109, Stroudsburg, PA, USA. Association for Computational Linguistics. https://doi.org/10.18653/v1/K17-2011
The code is so messy that other developers can not use this library. But the basic usage to replicate the result is as followings (CUDA GPU required):
Trainining ([dir_to_all] represents the path to the directory "all" in the official shared task dataset):
THEANO_FLAGS=\'device=cuda0\' python3 main.py train french --resource high \
--debug-print True --hidden-dim 200 --embedding-dim 300 --context-dim 200 \
--optimizer=adamax --max-iter 100 --model-save-interval 10000 \
--base-dir [dir_to_all]This will create a file like model/task1/french-high-30000-model.
Prediction:
THEANO_FLAGS=\'device=cuda0\' python3 main.py predict french --resource high \
--debug-print True --model-path model/task1/french-high-30000-model \
--base-dir [dir_to_all]This will create an output file like `out/task1/french-high-out'.
The directory results contains our final results submitted to the shared task.
License: new BSD.