README.md

Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders

(c) Simon Šuster, 2016

This is the implementation of the embedding models described in:

Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders. Simon Šuster, Ivan Titov and Gertjan van Noord. NAACL, 2016. bibtex

The individual similarity scores, presented as averages in the paper, are reported in appendix.

Bilingual training of the multi-sense model

See python3.4 examples/run_bimu.py --help for the full list of options, and set the Theano flags as THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32.

To train a multi-sense model bilingually on a toy parallel corpus of 10k sentences with default parameters:

python3.4 examples/run_bimu.py -model bimu -corpus_path_e data/toy_10k_en -corpus_path_f data/toy_10k_fr -corpus_path_a data/toy_10k_align -model_f_dir $OUTPUT_FR

This will output the embedding matrices, the vocabulary and the configuration file in the output/bimu3_toy_10k_en_... directory ($OUTPUT). Note that this presupposes that the second-language embeddings already exist in the folder $OUTPUT_FR. If not, train them by simply running:

python3.4 examples/run_mono.py -corpus_path data/toy_10k_fr -model sg

To obtain the nearest neighbors for selected polysemous words:

python3 eval/neighbors.py -input_dir $OUTPUT

To evaluate the embeddings on the SCWS dataset:

python3 eval/scws/embed.py -input_dir $OUTPUT -model senses -sim avg_exp

To train and test the POS tagger:

python3 eval/nn/score.py -train_file wsjtrain -test_file wsjtest  
-tag_vocab_file data/tagvocab.json -embedding_file $OUTPUT/W_w.npy -vocab_file $OUTPUT/w_index.json -cembedding_file $OUTPUT/W_c.npy

Here, you will need the gold standard WSJ data available as wsjtrain and wsjtest. The index of POS tags is given as a json file, example can be found in data/tagvocab.json.

Training the monolingual Skip-Gram and multi-sense models

See python3.4 examples/run_mono.py --help for the full list of options, and set the Theano flags as THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32.

To train the basic SkipGram with default options:

python3.4 examples/run_mono.py -corpus_path data/toy_10k_en -model sg

To train a multi-sense embedding model with 3 senses per word:

python3.4 examples/run_mono.py -corpus_path data/toy_10k_en -model senses -n_senses 3