I am confused on the inputs of a Long-Short Term Memory (LSTM) for the slot filling task in Spoken Language Understanding.
Before I worked on this, I implemented a language model with a Recurrent Neural Network (RNN) and then with a LSTM. The input to the RNN and LSTM language models was a one hot vector, which represented each word.
Now, when moving on to the slot filling task for a LSTM, I am having trouble what the input would be. I know that a one-hot vector representation is not enough for this task because the outputs along each time step are slot labels. I have a dictionary (in Python) that maps words to indices (which I can turn into a one hot vector), and I also have a dictionary with a labels (that are used for slot filling), which I got from the ATIS data. Here is an example:
I know I need the above two dictionaries to accomplish the slot filling task, but I cannot figure out how to use them as inputs for the LSTM? Furthermore, I have been using the basic LSTM structure, and for the language model LSTM I build, the output at each time step went through a Softmax function. Is this what will be required for slot filling too?
I am in high school and do not have anyone to contact, so any help is really appreciated. Thank you so much.
