import copy import json import os import re from collections import Counter from pprint import pprint from shutil import copy2, rmtree from zipfile import ZIP_DEFLATED, ZipFile from tqdm import tqdm ontology = { "domains": { "景点": { "description": "查找景点", "slots": { "名称": { "description": "景点名称", "is_categorical": False, "possible_values": [] }, "门票": { "description": "景点门票价格", "is_categorical": False, "possible_values": [] }, "游玩时间": { "description": "景点游玩时间", "is_categorical": False, "possible_values": [] }, "评分": { "description": "景点评分", "is_categorical": False, "possible_values": [] }, "地址": { "description": "景点地址", "is_categorical": False, "possible_values": [] }, "电话": { "description": "景点电话", "is_categorical": False, "possible_values": [] }, "周边景点": { "description": "景点周边景点", "is_categorical": False, "possible_values": [] }, "周边餐馆": { "description": "景点周边餐馆", "is_categorical": False, "possible_values": [] }, "周边酒店": { "description": "景点周边酒店", "is_categorical": False, "possible_values": [] } } }, "餐馆": { "description": "查找餐馆", "slots": { "名称": { "description": "餐馆名称", "is_categorical": False, "possible_values": [] }, "推荐菜": { "description": "餐馆推荐菜", "is_categorical": False, "possible_values": [] }, "人均消费": { "description": "餐馆人均消费", "is_categorical": False, "possible_values": [] }, "评分": { "description": "餐馆评分", "is_categorical": False, "possible_values": [] }, "地址": { "description": "餐馆地址", "is_categorical": False, "possible_values": [] }, "电话": { "description": "餐馆电话", "is_categorical": False, "possible_values": [] }, "营业时间": { "description": "餐馆营业时间", "is_categorical": False, "possible_values": [] }, "周边景点": { "description": "餐馆周边景点", "is_categorical": False, "possible_values": [] }, "周边餐馆": { "description": "餐馆周边餐馆", "is_categorical": False, "possible_values": [] }, "周边酒店": { "description": "餐馆周边酒店", "is_categorical": False, "possible_values": [] } } }, "酒店": { "description": "查找酒店", "slots": { "名称": { "description": "酒店名称", "is_categorical": False, "possible_values": [] }, "酒店类型": { "description": "酒店类型", "is_categorical": True, "possible_values": [ '高档型', '豪华型', '经济型', '舒适型' ] }, "酒店设施": { "description": "酒店设施", "is_categorical": False, "possible_values": [] }, "价格": { "description": "酒店价格", "is_categorical": False, "possible_values": [] }, "评分": { "description": "酒店评分", "is_categorical": False, "possible_values": [] }, "地址": { "description": "酒店地址", "is_categorical": False, "possible_values": [] }, "电话": { "description": "酒店电话", "is_categorical": False, "possible_values": [] }, "周边景点": { "description": "酒店周边景点", "is_categorical": False, "possible_values": [] }, "周边餐馆": { "description": "酒店周边餐馆", "is_categorical": False, "possible_values": [] }, "周边酒店": { "description": "酒店周边酒店", "is_categorical": False, "possible_values": [] } } }, "地铁": { "description": "乘坐地铁从某地到某地", "slots": { "出发地": { "description": "地铁出发地", "is_categorical": False, "possible_values": [] }, "目的地": { "description": "地铁目的地", "is_categorical": False, "possible_values": [] }, "出发地附近地铁站": { "description": "出发地附近地铁站", "is_categorical": False, "possible_values": [] }, "目的地附近地铁站": { "description": "目的地附近地铁站", "is_categorical": False, "possible_values": [] } } }, "出租": { "description": "乘坐出租车从某地到某地", "slots": { "出发地": { "description": "出租出发地", "is_categorical": False, "possible_values": [] }, "目的地": { "description": "出租目的地", "is_categorical": False, "possible_values": [] }, "车型": { "description": "出租车车型", "is_categorical": True, "possible_values": [ "#CX" ] }, "车牌": { "description": "出租车车牌", "is_categorical": True, "possible_values": [ "#CP" ] } } }, "General": { "description": "通用领域", "slots": {} } }, "intents": { "Inform": { "description": "告知相关属性" }, "Request": { "description": "询问相关属性" }, "Recommend": { "description": "推荐搜索结果" }, "Select": { "description": "在附近搜索" }, "NoOffer": { "description": "未找到符合用户要求的结果" }, "bye": { "description": "再见" }, "thank": { "description": "感谢" }, "welcome": { "description": "不客气" }, "greet": { "description": "打招呼" }, }, "state": { "景点": { "名称": "", "门票": "", "游玩时间": "", "评分": "", "周边景点": "", "周边餐馆": "", "周边酒店": "", }, "餐馆": { "名称": "", "推荐菜": "", "人均消费": "", "评分": "", "周边景点": "", "周边餐馆": "", "周边酒店": "", }, "酒店": { "名称": "", "酒店类型": "", "酒店设施": "", "价格": "", "评分": "", "周边景点": "", "周边餐馆": "", "周边酒店": "", }, "地铁": { "出发地": "", "目的地": "", }, "出租": { "出发地": "", "目的地": "", } }, "dialogue_acts": { "categorical": {}, "non-categorical": {}, "binary": {} } } cnt_domain_slot = Counter() def convert_da(da_list, utt): ''' convert dialogue acts to required format :param da_dict: list of (intent, domain, slot, value) :param utt: user or system utt ''' global ontology, cnt_domain_slot converted_da = { 'categorical': [], 'non-categorical': [], 'binary': [] } for intent, domain, slot, value in da_list: # if intent in ['Inform', 'Recommend']: if intent == 'NoOffer': assert slot == 'none' and value == 'none' converted_da['binary'].append({ 'intent': intent, 'domain': domain, 'slot': '' }) elif intent == 'General': # intent=General, domain=thank/bye/greet/welcome assert slot == 'none' and value == 'none' converted_da['binary'].append({ 'intent': domain, 'domain': intent, 'slot': '' }) elif intent == 'Request': assert value == '' and slot != 'none' converted_da['binary'].append({ 'intent': intent, 'domain': domain, 'slot': slot }) elif '酒店设施' in slot: converted_da['binary'].append({ 'intent': intent, 'domain': domain, 'slot': f"{slot}-{value}" }) elif intent == 'Select': assert slot == '源领域' converted_da['binary'].append({ 'intent': intent, 'domain': domain, 'slot': f"{slot}-{value}" }) elif slot in ['酒店类型', '车型', '车牌']: assert intent in ['Inform', 'Recommend'] assert slot != 'none' and value != 'none' converted_da['categorical'].append({ 'intent': intent, 'domain': domain, 'slot': slot, 'value': value }) else: assert intent in ['Inform', 'Recommend'] assert slot != 'none' and value != 'none' matches = utt.count(value) if matches == 1: start = utt.index(value) end = start + len(value) converted_da['non-categorical'].append({ 'intent': intent, 'domain': domain, 'slot': slot, 'value': value, 'start': start, 'end': end }) cnt_domain_slot['have span'] += 1 else: # can not find span converted_da['non-categorical'].append({ 'intent': intent, 'domain': domain, 'slot': slot, 'value': value }) cnt_domain_slot['no span'] += 1 # cnt_domain_slot.setdefault(f'{domain}-{slot}', set()) # cnt_domain_slot[f'{domain}-{slot}'].add(value) return converted_da def transform_user_state(user_state): goal = [] for subgoal in user_state: gid, domain, slot, value, mentioned = subgoal if len(value) != 0: t = 'inform' else: t = 'request' if len(goal) < gid: goal.append({domain: {'inform': {}, 'request': {}}}) goal[gid-1][domain][t][slot] = [value, 'mentioned' if mentioned else 'not mentioned'] return goal def preprocess(): original_data_dir = '../../crosswoz' new_data_dir = 'data' os.makedirs(new_data_dir, exist_ok=True) for filename in os.listdir(os.path.join(original_data_dir,'database')): copy2(f'{original_data_dir}/database/{filename}', new_data_dir) global ontology dataset = 'crosswoz' splits = ['train', 'validation', 'test'] dialogues_by_split = {split: [] for split in splits} for split in ['train', 'val', 'test']: data = json.load(ZipFile(os.path.join(original_data_dir, f'{split}.json.zip'), 'r').open(f'{split}.json')) if split == 'val': split = 'validation' for ori_dialog_id, ori_dialog in data.items(): dialogue_id = f'{dataset}-{split}-{len(dialogues_by_split[split])}' # get user goal and involved domains goal = {'inform': {}, 'request': {}} goal["description"] = '\n'.join(ori_dialog["task description"]) cur_domains = [x[1] for i, x in enumerate(ori_dialog['goal']) if i == 0 or ori_dialog['goal'][i-1][1] != x[1]] dialogue = { 'dataset': dataset, 'data_split': split, 'dialogue_id': dialogue_id, 'original_id': ori_dialog_id, 'domains': cur_domains, 'goal': goal, 'user_state_init': transform_user_state(ori_dialog['goal']), 'type': ori_dialog['type'], 'turns': [], 'user_state_final': transform_user_state(ori_dialog['final_goal']) } for turn_id, turn in enumerate(ori_dialog['messages']): # skip error turns if ori_dialog_id == '2660' and turn_id in [8,9]: continue elif ori_dialog_id == '7467' and turn_id in [14,15]: continue elif ori_dialog_id == '11726' and turn_id in [4,5]: continue elif ori_dialog_id == '10550' and turn_id == 6: dialogue['user_state_final'] = dialogue['turns'][-2]['user_state'] break elif ori_dialog_id == '11724' and turn_id == 8: dialogue['user_state_final'] = dialogue['turns'][-2]['user_state'] break speaker = 'user' if turn['role'] == 'usr' else 'system' utt = turn['content'] das = turn['dialog_act'] dialogue_acts = convert_da(das, utt) dialogue['turns'].append({ 'speaker': speaker, 'utterance': utt, 'utt_idx': len(dialogue['turns']), 'dialogue_acts': dialogue_acts, }) # add to dialogue_acts dictionary in the ontology for da_type in dialogue_acts: das = dialogue_acts[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'])][speaker] = True if speaker == 'user': dialogue['turns'][-1]['user_state'] = transform_user_state(turn['user_state']) else: # add state to last user turn belief_state = turn['sys_state_init'] for domain in belief_state: belief_state[domain].pop('selectedResults') dialogue['turns'][-2]['state'] = belief_state db_query = turn['sys_state'] db_results = {} for domain in list(db_query.keys()): db_res = db_query[domain].pop('selectedResults') if len(db_res) > 0: db_results[domain] = [{'名称': x} for x in db_res] else: db_query.pop(domain) dialogue['turns'][-1]['db_query'] = db_query dialogue['turns'][-1]['db_results'] = db_results dialogues_by_split[split].append(dialogue) pprint(cnt_domain_slot.most_common()) 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(new_data_dir) return dialogues, ontology def fix_entity_booked_info(entity_booked_dict, booked): for domain in entity_booked_dict: if not entity_booked_dict[domain] and booked[domain]: entity_booked_dict[domain] = True booked[domain] = [] return entity_booked_dict, booked if __name__ == '__main__': preprocess()