Does the corpus size affect the mapping learned?

[iamsainianuj]

I have corpus pair of 2 languages viz. Hindi and English but the corpus is not much large having only 78391 vectors(49806_eng + 28585_hin) in total as separate monolingual embeddings got as a result of fasttext training.

Now when i try to run the evaluate.py script i get very poor results by following command
python3 evaluate.py --src_lang en --tgt_lang hi --src_emb dumped/debug/eng-hin/vectors-en.txt --tgt_emb dumped/debug/eng-hin/vectors-hi.txt --max_vocab 200000

the results are :

============ Initialized logger ============
INFO - 05/31/20 14:35:24 - 0:00:00 - cuda: True
dico_eval: default
emb_dim: 300
exp_id:
exp_name: debug
exp_path: /home/anuj/MUSE/dumped/debug/zgz7rlm5p8
max_vocab: 200000
normalize_embeddings:
src_emb: dumped/debug/eng-hin/vectors-en.txt
src_lang: en
tgt_emb: dumped/debug/eng-hin/vectors-hi.txt
tgt_lang: hi
verbose: 2
INFO - 05/31/20 14:35:24 - 0:00:00 - The experiment will be stored in /home/anuj/MUSE/dumped/debug/zgz7rlm5p8
INFO - 05/31/20 14:35:27 - 0:00:03 - Loaded 49806 pre-trained word embeddings.
INFO - 05/31/20 14:35:32 - 0:00:08 - Loaded 28585 pre-trained word embeddings.
INFO - 05/31/20 14:35:32 - 0:00:08 - ====================================================================
INFO - 05/31/20 14:35:32 - 0:00:08 - Dataset Found Not found Rho
INFO - 05/31/20 14:35:32 - 0:00:08 - ====================================================================
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_SEMEVAL17 263 125 0.4523
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_MTurk-771 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_VERB-143 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_MTurk-287 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_RG-65 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_YP-130 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_RW-STANFORD 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_MEN-TR-3k 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_WS-353-SIM 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_SIMLEX-999 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_WS-353-REL 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_MC-30 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - EN_WS-353-ALL 0 1 nan
INFO - 05/31/20 14:35:33 - 0:00:08 - ====================================================================
INFO - 05/31/20 14:35:33 - 0:00:08 - Monolingual source word similarity score average: nan
INFO - 05/31/20 14:35:33 - 0:00:08 - Found 1054 pairs of words in the dictionary (846 unique). 978 other pairs contained at least one unknown word (259 in lang1, 924 in lang2)
INFO - 05/31/20 14:35:33 - 0:00:08 - 846 source words - nn - Precision at k = 1: 0.000000
INFO - 05/31/20 14:35:33 - 0:00:08 - 846 source words - nn - Precision at k = 5: 0.118203
INFO - 05/31/20 14:35:33 - 0:00:08 - 846 source words - nn - Precision at k = 10: 0.118203
INFO - 05/31/20 14:35:33 - 0:00:08 - Found 1054 pairs of words in the dictionary (846 unique). 978 other pairs contained at least one unknown word (259 in lang1, 924 in lang2)
INFO - 05/31/20 14:35:33 - 0:00:09 - 846 source words - csls_knn_10 - Precision at k = 1: 0.000000
INFO - 05/31/20 14:35:33 - 0:00:09 - 846 source words - csls_knn_10 - Precision at k = 5: 0.000000
INFO - 05/31/20 14:35:33 - 0:00:09 - 846 source words - csls_knn_10 - Precision at k = 10: 0.000000

Am i doing anything wrong or it is just the size of corpus which is affecting the results..

Kindly give response/comment over this issue..

Thank you