How to improve deeplearning model performance?

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Python

tensorflow

deep-learning

, +

Using early stopping my model stops training at around 7 epochs because of overfitting

MAX_SEQUENCE_LENGTH = 1000
MAX_NUM_WORDS = 20000
EMBEDDING_DIM = 100
VALIDATION_SPLIT = 0.2
output_nodes = 759

embedding_layer = Embedding(num_words,
                            EMBEDDING_DIM,
                            embeddings_initializer=Constant(embedding_matrix),
                            input_length=MAX_SEQUENCE_LENGTH,
                            trainable=False)

print('Training model.')
output_nodes = y_train.shape[1]
# train a 1D convnet with global maxpooling
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
x = Conv1D(128, 5, activation='relu')(embedded_sequences)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = GlobalMaxPooling1D()(x)
x = Dense(128, activation='relu')(x)
preds = Dense(output_nodes, activation='softmax')(x)

model = Model(sequence_input, preds)
model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['acc'])

What I have tried:

I tried increasing the input nodes, reducing the batch size and using the k-folds method to improve performance. However, can't get the accuracy to cross 50%. Any thoughts on how can I achieve higher accuracy? I am trying to predict authors based on text. My data has 98k rows