Code for our ACL Findings 2021 paper,
"Combining Static Word Embedding and Contextual Representations for Bilingual Lexicon Induction".
python >= 3.6
numpy >= 1.9.0
pytorch >= 1.0
CUDA_VISIBLE_DEVICES=0 python train.py --src_lang $lg --tgt_lang en\
--static_src_emb_path $ssemb --static_tgt_emb_path $stemb\
--context_src_emb_path $csemb --context_tgt_emb_path $ctemb\
--train_data_path $data_path --save_path $save_path
--static_src_emb_path aligned source static embedding path
--static_tgt_emb_path aligned target static embedding path
--context_src_emb_path source context embedding path
--context_tgt_emb_path target context embedding path
CUDA_VISIBLE_DEVICES=0 python test_on_all_word.py --src_lang $lg\
--tgt_lang en --model_path $model_path\
--dict_path $dict_path\
--vecmap_context_src_emb_path $vcpath\
--vecmap_context_tgt_emb_path $vspath\
--vecmap
--vecmap_context_src_emb_path aligned source context embedding path
--vecmap_context_tgt_emb_path aligned target context embedding path
--vecmap use interpolation method, else unified method
lg=ar
CUDA_VISIBLE_DEVICES=0 python train.py --src_lang en --tgt_lang $lg\
--static_src_emb_path $ssemb --static_tgt_emb_path $stemb\
--context_src_emb_path $csemb --context_tgt_emb_path $ctemb\
--save_path $save_path
--static_src_emb_path aligned source static embedding path
--static_tgt_emb_path aligned target static embedding path
--context_src_emb_path source context embedding path
--context_tgt_emb_path target context embedding path
src=ar
tgt=en
model_path=../checkpoints/$src-$tgt-add_orign_nw.pkl_last
CUDA_VISIBLE_DEVICES=0 python test.py --model_path $model_path \
--dict_path ../$src-$tgt.5000-6500.txt --mode v2 \
--src_lang $src --tgt_lang $tgt \
--reload_src_ctx $path1 \
--reload_tgt_ctx $path2 --lambda_w1 0.11
--mode type use v1 for unified method and v2 for interpolated
--lambda_w1 the weight for interpolation
--reload_src_ctx aligned source context embedding
--reload_tgt_ctx aligned targte context embedding