Gathers statistics for the whole sets.

Get the slot name that is consistent with the schema file.

Evaluates the dialogue created by the model. First we load the user goal of the dialogue, then for each turn generated by the system w

Evaluation of the real dialogue. First we loads the user goal and then go through the dialogue history. Similar to evaluateGeneratedDi

Parses user goal into dictionary format.

Add information about availability of the booking option.

Create database pointer for all related domains.

Cleaning procedure for all kinds of errors in text and annotation.

Build dictionaries for both user and system sides. You can specify the size of the dictionary through DICT_SIZE variable.

Main function of the script - loads delexical dictionary, goes through each dialogue and does: 1) data normalization 2) delexicalization

Based on the belief state, we can find reference number that during data gathering was created randomly.

Given test and validation sets, divide the data for three different sets

Given system dialogue acts fix automatic delexicalization.

Correct the dialogue states in place.

:param hidden: previous hidden state of the decoder, in shape (layers*directions,B,H) :param encoder_outputs:

Given the user sentence, user belief state and database pointer, encode the sentence, decide what policy vector construct and feed it

Extract a dialogue from the file

Based on the mturk annotations we form multi-domain belief state

forward procedure. **No need for inputs to be sorted** :param input_seqs: Variable of [T,B] :param hidden: :param inp

Return number of available entities for particular domain.

Returns the list of entities for a given domain based on the annotation of the belief state