Source code for the paper "Morphological Inflection Generation with Hard Monotonic Attention"
Source code for the paper: Morphological Inflection Generation with Hard Monotonic Attention.
Requires dynet. You should compile the aligner before running hard_attention.py
by
running make
while in the src
directory.
Usage:
hard_attention.py [--dynet-mem MEM][--input=INPUT] [--hidden=HIDDEN] [--feat-input=FEAT] [--epochs=EPOCHS] [--layers=LAYERS] [--optimization=OPTIMIZATION] [--reg=REGULARIZATION][--learning=LEARNING] [--plot] [--eval] [--ensemble=ENSEMBLE] TRAIN_PATH DEV_PATH TEST_PATH RESULTS_PATH SIGMORPHON_PATH...
Arguments:
Options:
For example:
python hard_attention.py --dynet-mem 4096 --input=100 --hidden=100 --feat-input=20 --epochs=100 --layers=2 --optimization=ADADELTA /Users/roeeaharoni/research_data/sigmorphon2016-master/data/navajo-task1-train /Users/roeeaharoni/research_data/sigmorphon2016-master/data/navajo-task1-dev /Users/roeeaharoni/research_data/sigmorphon2016-master/data/navajo-task1-test /Users/roeeaharoni/Dropbox/phd/research/morphology/inflection_generation/results/navajo_results.txt /Users/roeeaharoni/research_data/sigmorphon2016-master/
If you use this code for research purposes, please use the following citation:
@InProceedings{aharoni-goldberg:2017:Long,
author = {Aharoni, Roee and Goldberg, Yoav},
title = {Morphological Inflection Generation with Hard Monotonic Attention},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {July},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics},
pages = {2004--2015},
abstract = {We present a neural model for morphological inflection generation which employs
a hard attention mechanism, inspired by the nearly-monotonic alignment commonly
found between the characters in a word and the characters in its inflection. We
evaluate the model on three previously studied morphological inflection
generation datasets and show that it provides state of the art results in
various setups compared to previous neural and non-neural approaches. Finally
we present an analysis of the continuous representations learned by both the
hard and soft (Bahdanau, 2014) attention models for the task, shedding some
light on the features such models extract.},
url = {http://aclweb.org/anthology/P17-1183}
}