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import copy
import json
import os
from zipfile import ZipFile, ZIP_DEFLATED
from shutil import rmtree
ontology = {
'domains': {
'restaurant': {
'description': 'search for a restaurant to dine',
'slots': {
'food': {
'description': 'food type of the restaurant',
'is_categorical': False,
'possible_values': []
},
'area': {
'description': 'area of the restaurant',
'is_categorical': True,
'possible_values': ["east", "west", "centre", "north", "south"]
},
'postcode': {
'description': 'postal code of the restaurant',
'is_categorical': False,
'possible_values': []
},
'phone': {
'description': 'phone number of the restaurant',
'is_categorical': False,
'possible_values': []
},
'address': {
'description': 'address of the restaurant',
'is_categorical': False,
'possible_values': []
},
'price range': {
'description': 'price range of the restaurant',
'is_categorical': True,
'possible_values': ["expensive", "moderate", "cheap"]
},
'name': {
'description': 'name of the restaurant',
'is_categorical': False,
'possible_values': []
}
}
}
},
'intents': {
'inform': {
'description': 'system informs user the value of a slot'
},
'request': {
'description': 'system asks the user to provide value of a slot'
}
},
'state': {
'restaurant': {
'food': '',
'area': '',
'postcode': '',
'phone': '',
'address': '',
'price range': '',
'name': ''
}
},
"dialogue_acts": {
"categorical": {},
"non-categorical": {},
"binary": {}
}
}
def convert_da(da, utt):
global ontology
converted = {
'binary': [],
'categorical': [],
'non-categorical': []
}
for s, v in da:
if s == 'request':
converted['binary'].append({
'intent': 'request',
'domain': 'restaurant',
'slot': v,
})
else:
slot_type = 'categorical' if ontology['domains']['restaurant']['slots'][s]['is_categorical'] else 'non-categorical'
v = v.strip()
if v != 'dontcare' and ontology['domains']['restaurant']['slots'][s]['is_categorical']:
if v == 'center':
v = 'centre'
elif v == 'east side':
v = 'east'
assert v in ontology['domains']['restaurant']['slots'][s]['possible_values'], print([s,v, utt])
converted[slot_type].append({
'intent': 'inform',
'domain': 'restaurant',
'slot': s,
'value': v
})
if slot_type == 'non-categorical' and v != 'dontcare':
start = utt.lower().find(v)
if start != -1:
end = start + len(v)
converted[slot_type][-1]['start'] = start
converted[slot_type][-1]['end'] = end
converted[slot_type][-1]['value'] = utt[start:end]
return converted
def preprocess():
original_data_dir = 'woz'
new_data_dir = 'data'
os.makedirs(new_data_dir, exist_ok=True)
dataset = 'woz'
splits = ['train', 'validation', 'test']
domain = 'restaurant'
dialogues_by_split = {split: [] for split in splits}
global ontology
for split in splits:
if split != 'validation':
filename = os.path.join(original_data_dir, f'woz_{split}_en.json')
else:
filename = os.path.join(original_data_dir, 'woz_validate_en.json')
if not os.path.exists(filename):
raise FileNotFoundError(
f'cannot find {filename}, should manually download from https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz')
data = json.load(open(filename))
for item in data:
dialogue = {
'dataset': dataset,
'data_split': split,
'dialogue_id': f'{dataset}-{split}-{len(dialogues_by_split[split])}',
'original_id': item['dialogue_idx'],
'domains': [domain],
'turns': []
}
turns = item['dialogue']
n_turn = len(turns)
for i in range(n_turn):
sys_utt = turns[i]['system_transcript'].strip()
usr_utt = turns[i]['transcript'].strip()
usr_da = turns[i]['turn_label']
for s, v in usr_da:
if s == 'request':
assert v in ontology['domains']['restaurant']['slots']
else:
assert s in ontology['domains']['restaurant']['slots']
if i != 0:
dialogue['turns'].append({
'utt_idx': len(dialogue['turns']),
'speaker': 'system',
'utterance': sys_utt,
})
cur_state = copy.deepcopy(ontology['state'])
for act_slots in turns[i]['belief_state']:
act, slots = act_slots['act'], act_slots['slots']
if act == 'inform':
for s, v in slots:
v = v.strip()
if v != 'dontcare' and ontology['domains']['restaurant']['slots'][s]['is_categorical']:
if v not in ontology['domains']['restaurant']['slots'][s]['possible_values']:
if v == 'center':
v = 'centre'
elif v == 'east side':
v = 'east'
assert v in ontology['domains']['restaurant']['slots'][s]['possible_values']
cur_state[domain][s] = v
cur_usr_da = convert_da(usr_da, usr_utt)
# add to dialogue_acts dictionary in the ontology
for da_type in cur_usr_da:
das = cur_usr_da[da_type]
for da in das:
ontology["dialogue_acts"][da_type].setdefault((da['intent'], da['domain'], da['slot']), {})
ontology["dialogue_acts"][da_type][(da['intent'], da['domain'], da['slot'])]['user'] = True
dialogue['turns'].append({
'utt_idx': len(dialogue['turns']),
'speaker': 'user',
'utterance': usr_utt,
'state': cur_state,
'dialogue_acts': cur_usr_da,
})
dialogues_by_split[split].append(dialogue)
dialogues = []
for split in splits:
dialogues += dialogues_by_split[split]
for da_type in ontology['dialogue_acts']:
ontology["dialogue_acts"][da_type] = sorted([str(
{'user': speakers.get('user', False), 'system': speakers.get('system', False), 'intent': da[0],
'domain': da[1], 'slot': da[2]}) for da, speakers in ontology["dialogue_acts"][da_type].items()])
json.dump(dialogues[:10], open(f'dummy_data.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
json.dump(ontology, open(f'{new_data_dir}/ontology.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
json.dump(dialogues, open(f'{new_data_dir}/dialogues.json', 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
with ZipFile('data.zip', 'w', ZIP_DEFLATED) as zf:
for filename in os.listdir(new_data_dir):
zf.write(f'{new_data_dir}/{filename}')
rmtree(original_data_dir)
rmtree(new_data_dir)
return dialogues, ontology
if __name__ == '__main__':
preprocess()
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