Week 8: 10/23 – 10/27

For this week, I worked mostly on trying other arguments to get a better performance on my dataset.

I played around with the configuration below:

{
“dropout_rate”: 0.1,
“suffix”: “quora”,
“NER_dim”: 20,
“highway_layer_num”: 1,
“with_match_highway”: true,
“optimize_type”: “adam”,
“with_highway”: true,
“max_epochs”: 10,
“with_aggregation_highway”: true,
“with_filter_layer”: false,
“lex_decompsition_dim”: -1,
“aggregation_layer_num”: 1,
“max_char_per_word”: 10,
“wo_maxpool_match”: false,
“context_layer_num”: 1,
“wo_full_match”: false,
“lambda_l2”: 0.0,
“fix_word_vec”: true,
“wo_left_match”: false,
“with_NER”: false,
“aggregation_lstm_dim”: 300,
“context_lstm_dim”: 100,
“POS_dim”: 20,
“with_lex_decomposition”: false,
“learning_rate”: 0.001,
“with_POS”: false,
“wo_right_match”: false,
“MP_dim”: 10,
“max_sent_length”: 100,
“batch_size”: 60,
“wo_max_attentive_match”: false,
“wo_char”: false,
“wo_attentive_match”: false,
“char_emb_dim”: 20,
“char_lstm_dim”: 100,
“word_level_MP_dim”: -1,
“base_dir”: “./quora”
}

I also looked into doing more research about how I can go about transforming the csv file of sentences into the proper tsv format that we will need to be able to run it properly using the Sentence Match Decoder.