- This repository is about Subword-based Compact Reconstruction of Word Embeddings. Sasaki et al. NAACL2019
- Python version >= 3.7
- chainer
- numpy
$ python src/train.py \
--gpu 0 \
--ref_vec_path crawl-300d-2M-subword.vec \
--freq_path resources/freq_count.crawl-300d-2M-subword.txt \
--multi_hash two \
--maxlen 200 \
--codecs_path resources/ngram_dic.max30.min3 \
--network_type 2 \
--subword_type 4 \
--limit_size 1000000 \
--bucket_size 100000 \
--result_dir ./result \
--hashed_idx \
--unique_false
| network_type | subword_type | hashed_idx | |
|---|---|---|---|
| SUM-F | 2 | 0 | ✘ |
| SUM-H | 2 | 0 | ✓ |
| KVQ-H | 3 | 0 | ✓ |
| SUM-FH | 2 | 4 | ✓ |
| KVQ-FH | 3 | 4 | ✓ |
For estimating OOV word vectors:
$ python src/inference.py \
--gpu 0 \
--model_path \
result/sum/20190625_00_57_18/model_epoch_300 \
--codecs_path resources/ngram_dic.max30.min3 \
--oov_word_path resources/oov_words.txt
For reconstructing original word embeddings:
$ python src/save_embedding.py \
--gpu 0 \
--inference \
--model_path result/sum/20190625_00_57_18/model_epoch_300
- See resource page
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