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()