def createDelexData():
"""Main function of the script - loads delexical dictionary,
goes through each dialogue and does:
1) data normalization
2) delexicalization
3) addition of database pointer
4) saves the delexicalized data
"""
# create dictionary of delexicalied values that then we will search against, order matters here!
dic = delexicalize.prepareSlotValuesIndependent()
delex_data = {}
fin1 = open('data/woz2/data.json')
data = json.load(fin1)
for dialogue_name in tqdm(data):
if 'WOZ' not in dialogue_name:
continue
dialogue = data[dialogue_name]
#print dialogue_name
for idx, turn in enumerate(dialogue['log']):
# normalization, split and delexicalization of the sentence
sent = normalize(turn['text'])
words = sent.split()
sent = delexicalize.delexicalise(' '.join(words), dic)
# changes to numbers only here
digitpat = re.compile('\d+')
sent = re.sub(digitpat, '[value_count]', sent)
# delexicalized sentence added to the dialogue
dialogue['log'][idx]['text'] = sent
if idx % 2 == 1: # if it's a system turn
# add database pointer
pointer_vector = addDBPointer(turn)
#print pointer_vector
dialogue['log'][idx -
1]['db_pointer'] = pointer_vector.tolist()
delex_data[dialogue_name] = dialogue
with open('data/delex.json', 'w') as outfile:
json.dump(delex_data, outfile)
return delex_data
def createDelexData(sent, sent_act, bs, dic, turn, option):
# normalization, split and delexicalization of the sentence
sent = normalize(sent)
words = sent.split()
sent = delexicalize.delexicalise(' '.join(words), dic)
# parsing reference number GIVEN belief state
sent = delexicaliseReferenceNumber(sent, turn)
# changes to numbers only here
digitpat = re.compile('\d+')
sent = re.sub(digitpat, '[value_count]', sent)
if option == 'user':
sent = fixDelex(sent, None, bs)
if option == 'sys':
sent = fixDelex(sent, sent_act, None)
return sent.strip()
def createDelexData():
"""Main function of the script - loads delexical dictionary,
goes through each dialogue and does:
1) data normalization
2) delexicalization
3) addition of database pointer
4) saves the delexicalized data
"""
# download the data
loadData()
# create dictionary of delexicalied values that then we will search against, order matters here!
dic = delexicalize.prepareSlotValuesIndependent()
delex_data = {}
with open('data/multi-woz/data.json') as fin1:
data = json.load(fin1)
with open('data/multi-woz/dialogue_acts.json') as fin2:
data2 = json.load(fin2)
cnt = 10
for dialogue_name in tqdm(data):
dialogue = data[dialogue_name]
# print(dialogue_name)
idx_acts = 1
for idx, turn in enumerate(dialogue['log']):
# normalization, split and delexicalization of the sentence
sent = normalize(turn['text'])
words = sent.split()
sent = delexicalize.delexicalise(' '.join(words), dic)
# parsing reference number GIVEN belief state
sent = delexicaliseReferenceNumber(sent, turn)
# changes to numbers only here
digitpat = re.compile('\d+')
sent = re.sub(digitpat, '[value_count]', sent)
# delexicalized sentence added to the dialogue
dialogue['log'][idx]['text'] = sent
if idx % 2 == 1: # if it's a system turn
# add database pointer
pointer_vector = addDBPointer(turn)
# add booking pointer
pointer_vector = addBookingPointer(dialogue, turn,
pointer_vector)
# print(pointer_vector)
dialogue['log'][idx -
1]['db_pointer'] = pointer_vector.tolist()
# FIXING delexicalization:
dialogue = fixDelex(dialogue_name, dialogue, data2, idx, idx_acts)
idx_acts += 1
delex_data[dialogue_name] = dialogue
with open('data/multi-woz/delex.json', 'w') as outfile:
json.dump(delex_data, outfile)
return delex_data