# 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