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tm1 / preprocess.py
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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
descriptions = {
"uber_lyft": {
"uber_lyft": "order a car for a ride inside a city",
"location.from": "pickup location",
"location.to": "destination of the ride",
"type.ride": "type of ride",
"num.people": "number of people",
"price.estimate": "estimated cost of the ride",
"duration.estimate": "estimated duration of the ride",
"time.pickup": "time of pickup",
"time.dropoff": "time of dropoff",
},
"movie_ticket": {
"movie_ticket": "book movie tickets for a film",
"name.movie": "name of the movie",
"name.theater": "name of the theater",
"num.tickets": "number of tickets",
"time.start": "start time of the movie",
"location.theater": "location of the theater",
"price.ticket": "price of the ticket",
"type.screening": "type of the screening",
"time.end": "end time of the movie",
"time.duration": "duration of the movie",
},
"restaurant_reservation": {
"restaurant_reservation": "searching for a restaurant and make reservation",
"name.restaurant": "name of the restaurant",
"name.reservation": "name of the person who make the reservation",
"num.guests": "number of guests",
"time.reservation": "time of the reservation",
"type.seating": "type of the seating",
"location.restaurant": "location of the restaurant",
},
"coffee_ordering": {
"coffee_ordering": "order a coffee drink from either Starbucks or Peets for pick up",
"location.store": "location of the coffee store",
"name.drink": "name of the drink",
"size.drink": "size of the drink",
"num.drink": "number of drinks",
"type.milk": "type of the milk",
"preference": "user preference of the drink",
},
"pizza_ordering": {
"pizza_ordering": "order a pizza",
"name.store": "name of the pizza store",
"name.pizza": "name of the pizza",
"size.pizza": "size of the pizza",
"type.topping": "type of the topping",
"type.crust": "type of the crust",
"preference": "user preference of the pizza",
"location.store": "location of the pizza store",
},
"auto_repair": {
"auto_repair": "set up an auto repair appointment with a repair shop",
"name.store": "name of the repair store",
"name.customer": "name of the customer",
"date.appt": "date of the appointment",
"time.appt": "time of the appointment",
"reason.appt": "reason of the appointment",
"name.vehicle": "name of the vehicle",
"year.vehicle": "year of the vehicle",
"location.store": "location of the repair store",
}
}
def normalize_domain_name(domain):
if domain == 'auto':
return 'auto_repair'
elif domain == 'pizza':
return 'pizza_ordering'
elif domain == 'coffee':
return 'coffee_ordering'
elif domain == 'uber':
return 'uber_lyft'
elif domain == 'restaurant':
return 'restaurant_reservation'
elif domain == 'movie':
return 'movie_ticket'
assert 0
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'] == '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 tm1/, 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.'},
'accept': {'description': 'accept the value of a slot or a transaction'},
'reject': {'description': 'reject the value of a slot or a transaction'}
},
'state': {},
'dialogue_acts': {
"categorical": {},
"non-categorical": {},
"binary": {}
}}
global descriptions
ori_ontology = {}
for _, item in json.load(open(os.path.join(original_data_dir, "TM-1-2019/ontology.json"))).items():
ori_ontology[item["id"]] = item
for domain, item in ori_ontology.items():
ontology['domains'][domain] = {'description': descriptions[domain][domain], 'slots': {}}
ontology['state'][domain] = {}
for slot in item['required']+item['optional']:
ontology['domains'][domain]['slots'][slot] = {
'description': descriptions[domain][slot],
'is_categorical': False,
'possible_values': [],
}
ontology['state'][domain][slot] = ''
dataset = 'tm1'
splits = ['train', 'validation', 'test']
dialogues_by_split = {split:[] for split in splits}
dialog_files = ["TM-1-2019/self-dialogs.json", "TM-1-2019/woz-dialogs.json"]
for file_idx, filename in enumerate(dialog_files):
data = json.load(open(os.path.join(original_data_dir, filename)))
if file_idx == 0:
# original split for self dialogs
dial_id2split = {}
for data_split in ['train', 'dev', 'test']:
with open(os.path.join(original_data_dir, f"TM-1-2019/train-dev-test/{data_split}.csv")) as f:
for line in f:
dial_id = line.split(',')[0]
dial_id2split[dial_id] = data_split if data_split != 'dev' else 'validation'
else:
# random split for woz dialogs 8:1:1
random.seed(42)
dial_ids = [d['conversation_id'] for d in 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 d in tqdm(data, desc='processing taskmaster-{}'.format(filename)):
# 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[d["conversation_id"]]
dialogue_id = f'{dataset}-{data_split}-{len(dialogues_by_split[data_split])}'
cur_domains = [normalize_domain_name(d["instruction_id"].split('-', 1)[0])]
assert len(cur_domains) == 1 and cur_domains[0] in ontology['domains']
domain = cur_domains[0]
dialogue = {
'dataset': dataset,
'data_split': data_split,
'dialogue_id': dialogue_id,
'original_id': d["conversation_id"],
'domains': cur_domains,
'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:
# Each conversation was annotated by two workers.
# only keep the first annotation for the span
item = segment['annotations'][0]
intent = 'inform' # default intent
slot = item['name'].split('.', 1)[-1]
if slot.endswith('.accept') or slot.endswith('.reject'):
# intent=accept/reject
intent = slot[-6:]
slot = slot[:-7]
if slot not in ontology['domains'][domain]['slots']:
# no slot, only general reference to a transaction, binary dialog act
turn['dialogue_acts']['binary'].append({
'intent': intent,
'domain': domain,
'slot': '',
})
else:
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]
# not add reject slot-value into state
if da['intent'] != 'reject':
prev_state[domain][slot] = value
if speaker == 'user':
turn['state'] = copy.deepcopy(prev_state)
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()