[Discussion] Incorporating word embeddings to train LSTM • /r/MachineLearning

I am unable to train a network using my pretrained wording embeddings as weights for input layer to LSTM. My word2vec embedding is trained on a larger corpus and the training corpus is a subset of it. My vocabulary for the task consists of some word2vec_vocab additional words in corpus. Test data: one hot vector with 1 at position w.r.t corpus(I am not using full dict for mapping, thus position indices differ.) Problem: Model overfitting on training data with increasing loss on validation set.