The results using pre-trained word embedding is worse than the one using random embedding ?

[BlockheadLS]

It is very incredible that the version using pre-trained word embedding is worse than the one using random word embedding. I don't know if I had wrong configurations, configurations are as follows:
train.json:
{
"encoder": "gru",
"encoder_dim": 1200,
"bidir": true,
"case_sensitive": true,
"checkpoint_path": "",
"vocab_configs": [
{
"mode": "fixed",
"name": "word_embedding",
"cap": false,
"dim": 200,
"size": 1133884,
"vocab_file": "/nfs/private/FST/models/embeddings/glove.840B.300d.txt",
"embs_file": ""
}
]
}

run.sh:
RESULTS_HOME="results"
MDL_CFGS="model_configs"
GLOVE_PATH="/nfs/private/FST/models/embeddings/"

DATA_DIR="data/CS_10M_pretrained/TFRecords"
NUM_INST=10000000 # Number of sentences

CFG="CS_10M_pretrained"

BS=400
SEQ_LEN=30

export CUDA_VISIBLE_DEVICES=0
python src/train.py
--input_file_pattern="$DATA_DIR/train-?????-of-00100"
--train_dir="$RESULTS_HOME/$CFG/train"
--learning_rate_decay_factor=0
--batch_size=$BS
--sequence_length=$SEQ_LEN
--nepochs=1
--num_train_inst=$NUM_INST
--save_model_secs=1800
--Glove_path=$GLOVE_PATH
--model_config="$MDL_CFGS/$CFG/train.json" &

export CUDA_VISIBLE_DEVICES=1
python src/eval.py
--input_file_pattern="$DATA_DIR/validation-?????-of-00001"
--checkpoint_dir="$RESULTS_HOME/$CFG/train"
--eval_dir="$RESULTS_HOME/$CFG/eval"
--batch_size=$BS
--model_config="$MDL_CFGS/$CFG/train.json"
--eval_interval_secs=1800
--sequence_length=$SEQ_LEN &

[ChenXi1992]

Can you share the result that you get? I meet the same problem.