def delexicaliseReferenceNumber(sent, turn):
"""Based on the belief state, we can find reference number that
during data gathering was created randomly."""
domains = [
'restaurant', 'hotel', 'attraction', 'train', 'taxi', 'hospital'
] # , 'police']
if turn['metadata']:
for domain in domains:
if turn['metadata'][domain]['book']['booked']:
for slot in turn['metadata'][domain]['book']['booked'][0]:
if slot == 'reference':
val = '[' + domain + '_' + slot + ']'
else:
val = '[' + domain + '_' + slot + ']'
key = normalize(
turn['metadata'][domain]['book']['booked'][0][slot])
sent = (' ' + sent + ' ').replace(' ' + key + ' ',
' ' + val + ' ')
# try reference with hashtag
key = normalize(
"#" +
turn['metadata'][domain]['book']['booked'][0][slot])
sent = (' ' + sent + ' ').replace(' ' + key + ' ',
' ' + val + ' ')
# try reference with ref#
key = normalize(
"ref#" +
turn['metadata'][domain]['book']['booked'][0][slot])
sent = (' ' + sent + ' ').replace(' ' + key + ' ',
' ' + val + ' ')
return sent
def queryResultVenues(domain, turn, real_belief=False):
# query the db
sql_query = "select * from {}".format(domain)
if real_belief == True:
items = turn.items()
elif real_belief == 'tracking':
for slot in turn[domain]:
key = slot[0].split("-")[1]
val = slot[0].split("-")[2]
if key == "price range":
key = "pricerange"
elif key == "leave at":
key = "leaveAt"
elif key == "arrive by":
key = "arriveBy"
if val == "do n't care":
pass
else:
if flag:
sql_query += " where "
val2 = val.replace("'", "''")
val2 = normalize(val2)
if key == 'leaveAt':
sql_query += key + " > " + r"'" + val2 + r"'"
elif key == 'arriveBy':
sql_query += key + " < " + r"'" + val2 + r"'"
else:
sql_query += r" " + key + "=" + r"'" + val2 + r"'"
flag = False
else:
val2 = val.replace("'", "''")
val2 = normalize(val2)
if key == 'leaveAt':
sql_query += r" and " + key + " > " + r"'" + val2 + r"'"
elif key == 'arriveBy':
sql_query += r" and " + key + " < " + r"'" + val2 + r"'"
else:
sql_query += r" and " + key + "=" + r"'" + val2 + r"'"
try: # "select * from attraction where name = 'queens college'"
return dbs[domain].execute(sql_query).fetchall()
except:
return [] # TODO test it
pass
else:
items = turn['metadata'][domain]['semi'].items()
flag = True
for key, val in items:
if val == "" or val == "dontcare" or val == 'not mentioned' or val == "don't care" or val == "dont care" or val == "do n't care":
pass
else:
if flag:
sql_query += " where "
val2 = val.replace("'", "''")
val2 = normalize(val2)
if key == 'leaveAt':
sql_query += r" " + key + " > " + r"'" + val2 + r"'"
elif key == 'arriveBy':
sql_query += r" " + key + " < " + r"'" + val2 + r"'"
else:
sql_query += r" " + key + "=" + r"'" + val2 + r"'"
flag = False
else:
val2 = val.replace("'", "''")
val2 = normalize(val2)
if key == 'leaveAt':
sql_query += r" and " + key + " > " + r"'" + val2 + r"'"
elif key == 'arriveBy':
sql_query += r" and " + key + " < " + r"'" + val2 + r"'"
else:
sql_query += r" and " + key + "=" + r"'" + val2 + r"'"
try: # "select * from attraction where name = 'queens college'"
return dbs[domain].execute(sql_query).fetchall()
except:
raise
return [] # TODO test it
def predict(model, prev_state, prev_active_domain, state, dic):
start_time = time.time()
model.beam_search = False
input_tensor = []
bs_tensor = []
db_tensor = []
usr = state['history'][-1][-1]
prev_state = deepcopy(prev_state['belief_state'])
state = deepcopy(state['belief_state'])
mark_not_mentioned(prev_state)
mark_not_mentioned(state)
words = usr.split()
usr = delexicalize.delexicalise(' '.join(words), dic)
# parsing reference number GIVEN belief state
usr = delexicaliseReferenceNumber(usr, state)
# changes to numbers only here
digitpat = re.compile('\d+')
usr = re.sub(digitpat, '[value_count]', usr)
# dialogue = fixDelex(dialogue_name, dialogue, data2, idx, idx_acts)
# add database pointer
pointer_vector, top_results, num_results = addDBPointer(state)
# add booking pointer
pointer_vector = addBookingPointer(state, pointer_vector)
belief_summary = get_summary_bstate(state)
tensor = [
model.input_word2index(word)
for word in normalize(usr).strip(' ').split(' ')
] + [util.EOS_token]
input_tensor.append(torch.LongTensor(tensor))
bs_tensor.append(belief_summary) #
db_tensor.append(pointer_vector) # db results and booking
# bs_tensor.append([0.] * 94) #
# db_tensor.append([0.] * 30) # db results and booking
# create an empty matrix with padding tokens
input_tensor, input_lengths = util.padSequence(input_tensor)
bs_tensor = torch.tensor(bs_tensor, dtype=torch.float, device=device)
db_tensor = torch.tensor(db_tensor, dtype=torch.float, device=device)
output_words, loss_sentence = model.predict(input_tensor, input_lengths,
input_tensor, input_lengths,
db_tensor, bs_tensor)
active_domain = get_active_domain(prev_active_domain, prev_state, state)
if active_domain is not None and active_domain in num_results:
num_results = num_results[active_domain]
else:
num_results = 0
if active_domain is not None and active_domain in top_results:
top_results = {active_domain: top_results[active_domain]}
else:
top_results = {}
response = populate_template(output_words[0], top_results, num_results,
state)
return response, active_domain
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 = {}
# fin1 = file('data/multi-woz/data.json')
fin1 = open('data/multi-woz/data.json')
data = json.load(fin1)
# fin2 = file('data/multi-woz/dialogue_acts.json')
fin2 = open('data/multi-woz/dialogue_acts.json')
data2 = json.load(fin2)
num = 0
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
# num += 1
# if num > 100:
# break
with open('data/multi-woz/delex.json', 'w') as outfile:
json.dump(delex_data, outfile)
return delex_data
def prepareSlotValuesIndependent():
domains = [
'restaurant', 'hotel', 'attraction', 'train', 'taxi', 'hospital',
'police'
]
requestables = ['phone', 'address', 'postcode', 'reference', 'id']
dic = []
dic_area = []
dic_food = []
dic_price = []
# read databases
for domain in domains:
try:
fin = open('db/' + domain + '_db.json')
db_json = json.load(fin)
fin.close()
for ent in db_json:
for key, val in ent.items():
if val == '?' or val == 'free':
pass
elif key == 'address':
dic.append((normalize(val),
'[' + domain + '_' + 'address' + ']'))
if "road" in val:
val = val.replace("road", "rd")
dic.append((normalize(val),
'[' + domain + '_' + 'address' + ']'))
elif "rd" in val:
val = val.replace("rd", "road")
dic.append((normalize(val),
'[' + domain + '_' + 'address' + ']'))
elif "st" in val:
val = val.replace("st", "street")
dic.append((normalize(val),
'[' + domain + '_' + 'address' + ']'))
elif "street" in val:
val = val.replace("street", "st")
dic.append((normalize(val),
'[' + domain + '_' + 'address' + ']'))
elif key == 'name':
dic.append((normalize(val),
'[' + domain + '_' + 'name' + ']'))
if "b & b" in val:
val = val.replace("b & b", "bed and breakfast")
dic.append((normalize(val),
'[' + domain + '_' + 'name' + ']'))
elif "bed and breakfast" in val:
val = val.replace("bed and breakfast", "b & b")
dic.append((normalize(val),
'[' + domain + '_' + 'name' + ']'))
elif "hotel" in val and 'gonville' not in val:
val = val.replace("hotel", "")
dic.append((normalize(val),
'[' + domain + '_' + 'name' + ']'))
elif "restaurant" in val:
val = val.replace("restaurant", "")
dic.append((normalize(val),
'[' + domain + '_' + 'name' + ']'))
elif key == 'postcode':
dic.append((normalize(val),
'[' + domain + '_' + 'postcode' + ']'))
elif key == 'phone':
dic.append((val, '[' + domain + '_' + 'phone' + ']'))
elif key == 'trainID':
dic.append(
(normalize(val), '[' + domain + '_' + 'id' + ']'))
elif key == 'department':
dic.append((normalize(val),
'[' + domain + '_' + 'department' + ']'))
# NORMAL DELEX
elif key == 'area':
dic_area.append((normalize(val),
'[' + 'value' + '_' + 'area' + ']'))
elif key == 'food':
dic_food.append((normalize(val),
'[' + 'value' + '_' + 'food' + ']'))
elif key == 'pricerange':
dic_price.append(
(normalize(val),
'[' + 'value' + '_' + 'pricerange' + ']'))
else:
pass
# TODO car type?
except:
pass
if domain == 'hospital':
dic.append(
(normalize('Hills Rd'), '[' + domain + '_' + 'address' + ']'))
dic.append((normalize('Hills Road'),
'[' + domain + '_' + 'address' + ']'))
dic.append(
(normalize('CB20QQ'), '[' + domain + '_' + 'postcode' + ']'))
dic.append(('01223245151', '[' + domain + '_' + 'phone' + ']'))
dic.append(('1223245151', '[' + domain + '_' + 'phone' + ']'))
dic.append(('0122324515', '[' + domain + '_' + 'phone' + ']'))
dic.append((normalize('Addenbrookes Hospital'),
'[' + domain + '_' + 'name' + ']'))
elif domain == 'police':
dic.append(
(normalize('Parkside'), '[' + domain + '_' + 'address' + ']'))
dic.append(
(normalize('CB11JG'), '[' + domain + '_' + 'postcode' + ']'))
dic.append(('01223358966', '[' + domain + '_' + 'phone' + ']'))
dic.append(('1223358966', '[' + domain + '_' + 'phone' + ']'))
dic.append((normalize('Parkside Police Station'),
'[' + domain + '_' + 'name' + ']'))
# add at the end places from trains
# fin = file('db/' + 'train' + '_db.json')
fin = open('db/' + 'train' + '_db.json')
db_json = json.load(fin)
fin.close()
for ent in db_json:
for key, val in ent.items():
if key == 'departure' or key == 'destination':
dic.append(
(normalize(val), '[' + 'value' + '_' + 'place' + ']'))
# add specific values:
for key in [
'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday',
'sunday'
]:
dic.append((normalize(key), '[' + 'value' + '_' + 'day' + ']'))
# more general values add at the end
dic.extend(dic_area)
dic.extend(dic_food)
dic.extend(dic_price)
return dic
