from zipfile import ZipFile, ZIP_DEFLATED import json import os import copy import zipfile from tqdm import tqdm import re from collections import Counter from shutil import rmtree from convlab.util.file_util import read_zipped_json, write_zipped_json from pprint import pprint import random import glob descriptions = { 'movie': 'Book movie tickets for the user', 'name.movie': 'Name of the movie, e.g. Joker, Parasite, The Avengers', 'name.theater': 'Name of the theater, e.g. Century City, AMC Mercado 20', 'num.tickets': 'Number of tickets, e.g. two, me and my friend, John and I', 'time.preference': 'Preferred time or range, e.g. around 2pm, later in the evening, 4:30pm', 'time.showing': 'The showtimes published by the theater, e.g. 5:10pm, 8:30pm', 'date.showing': 'the date or day of the showing, e.g. today, tonight, tomrrow, April 12th.', 'location': 'The city, or city and state, zip code and sometimes more specific regions, e.g. downtown', 'type.screening': 'IMAX, Dolby, 3D, standard, or similar phrases for technology offerings', 'seating': 'Various phrases from specific "row 1" to "near the back", "on an aisle", etc.', 'date.release': 'Movie attribute published for the official movie release date.', 'price.ticket': 'Price per ticket', 'price.total': 'The total for the purchase of all tickets', 'name.genre': 'Includes a wide range from classic genres like action, drama, etc. to categories like "slasher" or series like Marvel or Harry Potter', 'description.plot': 'The movie synopsis or shorter description', 'description.other': 'Any other movie description that is not captured by genre, name, plot.', 'duration.movie': 'The movie runtime, e.g. 120 minutes', 'name.person': 'Names of actors, directors, producers but NOT movie characters', 'name.character': 'Character names like James Bond, Harry Potter, Wonder Woman', 'review.audience': 'The audience review', 'review.critic': 'Critic reviews like those from Rotten Tomatoes, IMDB, etc.', 'rating.movie': 'G, PG, PG-13, R, etc.', } anno2slot = { "movie": { "description.other": False, # transform to binary dialog act "description.plot": False, # too long, 19 words in avg. transform to binary dialog act } } def format_turns(ori_turns): # delete invalid turns and merge continuous turns new_turns = [] previous_speaker = None utt_idx = 0 for i, turn in enumerate(ori_turns): speaker = 'system' if turn['speaker'].upper() == 'ASSISTANT' else 'user' turn['speaker'] = speaker if turn['text'] == '(deleted)': continue if not previous_speaker: # first turn assert speaker != previous_speaker if speaker != previous_speaker: # switch speaker previous_speaker = speaker new_turns.append(copy.deepcopy(turn)) utt_idx += 1 else: # continuous speaking of the same speaker last_turn = new_turns[-1] # skip repeated turn if turn['text'] in ori_turns[i-1]['text']: continue # merge continuous turns index_shift = len(last_turn['text']) + 1 last_turn['text'] += ' '+turn['text'] if 'segments' in turn: last_turn.setdefault('segments', []) for segment in turn['segments']: segment['start_index'] += index_shift segment['end_index'] += index_shift last_turn['segments'] += turn['segments'] return new_turns def preprocess(): original_data_dir = 'Taskmaster-master' new_data_dir = 'data' if not os.path.exists(original_data_dir): original_data_zip = 'master.zip' if not os.path.exists(original_data_zip): raise FileNotFoundError(f'cannot find original data {original_data_zip} in tm3/, should manually download master.zip from https://github.com/google-research-datasets/Taskmaster/archive/refs/heads/master.zip') else: archive = ZipFile(original_data_zip) archive.extractall() os.makedirs(new_data_dir, exist_ok=True) ontology = {'domains': {}, 'intents': { 'inform': {'description': 'inform the value of a slot or general information.'} }, 'state': {}, 'dialogue_acts': { "categorical": {}, "non-categorical": {}, "binary": {} }} global descriptions global anno2slot ori_ontology = json.load(open(os.path.join(original_data_dir, "TM-3-2020/ontology/entities.json"))) assert len(ori_ontology) == 1 domain = list(ori_ontology.keys())[0] domain_ontology = ori_ontology[domain] ontology['domains'][domain] = {'description': descriptions[domain], 'slots': {}} ontology['state'][domain] = {} for slot in domain_ontology['required']+domain_ontology['optional']: ontology['domains'][domain]['slots'][slot] = { 'description': descriptions[slot], 'is_categorical': False, 'possible_values': [], } if slot not in anno2slot[domain]: ontology['state'][domain][slot] = '' dataset = 'tm3' splits = ['train', 'validation', 'test'] dialogues_by_split = {split:[] for split in splits} data_files = sorted(glob.glob(os.path.join(original_data_dir, f"TM-3-2020/data/*.json"))) for data_file in tqdm(data_files, desc='processing taskmaster-{}'.format(domain)): data = json.load(open(data_file)) # random split, train:validation:test = 8:1:1 random.seed(42) dial_ids = list(range(len(data))) random.shuffle(dial_ids) dial_id2split = {} for dial_id in dial_ids[:int(0.8*len(dial_ids))]: dial_id2split[dial_id] = 'train' for dial_id in dial_ids[int(0.8*len(dial_ids)):int(0.9*len(dial_ids))]: dial_id2split[dial_id] = 'validation' for dial_id in dial_ids[int(0.9*len(dial_ids)):]: dial_id2split[dial_id] = 'test' for dial_id, d in enumerate(data): # delete empty dialogs and invalid dialogs if len(d['utterances']) == 0: continue if len(set([t['speaker'] for t in d['utterances']])) == 1: continue data_split = dial_id2split[dial_id] dialogue_id = f'{dataset}-{data_split}-{len(dialogues_by_split[data_split])}' cur_domains = [domain] goal = { 'description': d['instructions'], 'inform': {}, 'request': {} } dialogue = { 'dataset': dataset, 'data_split': data_split, 'dialogue_id': dialogue_id, 'original_id': d["conversation_id"], 'domains': cur_domains, 'goal': goal, 'turns': [] } turns = format_turns(d['utterances']) prev_state = {} prev_state.setdefault(domain, copy.deepcopy(ontology['state'][domain])) for utt_idx, uttr in enumerate(turns): speaker = uttr['speaker'] turn = { 'speaker': speaker, 'utterance': uttr['text'], 'utt_idx': utt_idx, 'dialogue_acts': { 'binary': [], 'categorical': [], 'non-categorical': [], }, } in_span = [0] * len(turn['utterance']) if 'segments' in uttr: # sort the span according to the length segments = sorted(uttr['segments'], key=lambda x: len(x['text'])) for segment in segments: assert len(['annotations']) == 1 item = segment['annotations'][0] intent = 'inform' # default intent slot = item['name'].strip() assert slot in ontology['domains'][domain]['slots'] if slot in anno2slot[domain]: # binary dialog act turn['dialogue_acts']['binary'].append({ 'intent': intent, 'domain': domain, 'slot': slot, }) continue assert turn['utterance'][segment['start_index']:segment['end_index']] == segment['text'] # skip overlapped spans, keep the shortest one if sum(in_span[segment['start_index']: segment['end_index']]) > 0: continue else: in_span[segment['start_index']: segment['end_index']] = [1]*(segment['end_index']-segment['start_index']) turn['dialogue_acts']['non-categorical'].append({ 'intent': intent, 'domain': domain, 'slot': slot, 'value': segment['text'], 'start': segment['start_index'], 'end': segment['end_index'] }) turn['dialogue_acts']['non-categorical'] = sorted(turn['dialogue_acts']['non-categorical'], key=lambda x: x['start']) bdas = set() for da in turn['dialogue_acts']['binary']: da_tuple = (da['intent'], da['domain'], da['slot'],) bdas.add(da_tuple) turn['dialogue_acts']['binary'] = [{'intent':bda[0],'domain':bda[1],'slot':bda[2]} for bda in sorted(bdas)] # add to dialogue_acts dictionary in the ontology for da_type in turn['dialogue_acts']: das = turn['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 for da in turn['dialogue_acts']['non-categorical']: slot, value = da['slot'], da['value'] assert slot in prev_state[domain], print(da) prev_state[domain][slot] = value if speaker == 'user': turn['state'] = copy.deepcopy(prev_state) else: turn['db_results'] = {} if 'apis' in turns[utt_idx-1]: turn['db_results'].setdefault(domain, []) apis = turns[utt_idx-1]['apis'] turn['db_results'][domain] += apis dialogue['turns'].append(turn) dialogues_by_split[data_split].append(dialogue) 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()]) dialogues = dialogues_by_split['train']+dialogues_by_split['validation']+dialogues_by_split['test'] 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()