Code for the ACL2021 paper "Combining Static Word Embedding and Contextual Representations for Bilingual Lexicon Induction"

Related tags

Computer VisionCSCBLI
Overview

CSCBLI

Code for our ACL Findings 2021 paper,
"Combining Static Word Embedding and Contextual Representations for Bilingual Lexicon Induction".

Requirements

python >= 3.6
numpy >= 1.9.0
pytorch >= 1.0

Supervised

How to train

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

How to Test

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

Unsupervised

How to train

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

How to Test

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
Owner
Jinpeng Zhang
Jinpeng Zhang
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