# contextLSTMCNN
###########expADE.py##########
Usage: expADE.py [options]
Options:
-h, --help show this help message and exit
--txtFile=TXTFILE txt input file
--pklFile=PKLFILE pickled input file
--train=TRAIN train model
--test test model
--trainTestSplit split file into training (80%) and testing parts(20%)
--saveTrainTest=SAVETRAINTEST
save train test split
--saveModel=SAVEMODEL
save trained model
--epochs=EPOCHS number of epochs for training, default 50
--testIDs=TESTIDS load test IDs
--trainIDs=TRAINIDS load train IDs
--windowSize=WINDOWSSIZE
window size
--loadModel=LOADMODEL
load trianed model
--nFoldsSplit=NFOLDSSPLIT
prefix of n folds split
--sentenceLevelEvaluation
sentence level evaluation
--experiment compare with other models
--sentAlpha=SENTALPHA
sent alpha
--blstmBlstmcnn train blstmBlstmcnn
--loadW2V=LOADW2V load trianed w2v
--wordAlpha=WORDALPHA
word alpha
############expIEMOCAP.py############
Usage: expIEMOCAP.py [options]
Options:
-h, --help show this help message and exit
--transcriptions=TRANSCRIPTIONDIR
transcription directory
--evaluation=EVALUATIONDIR
evaluation directory
--train=TRAIN train model
--saveData train model
--test test model
--trainTestSplit split file into training (80%) and testing parts(20%)
--saveTrainTest=SAVETRAINTEST
save train test split
--nFoldsSplit=NFOLDSSPLIT
prefix of n folds split
--saveModel=SAVEMODEL
save trained model
--epochs=EPOCHS number of epochs for training, default 50
--testIDs=TESTIDS load test IDs
--trainIDs=TRAINIDS load train IDs
--windowSize=WINDOWSSIZE
window size
--loadModel=LOADMODEL
load trianed model
--sentAlpha=SENTALPHA
sent alpha
--wordAlpha=WORDALPHA
word alpha
--experiment compare with other models
--blstmBlstmcnn train blstmBlstmcnn
--loadW2V=LOADW2V load trianed w2v
Cite:
Xingyi Song, Johann Petrak, Angus Roberts. A Deep Neural Network Sentence Level
Classification Method with Context Information. In: EMNLP, 2018