class MultiWozReader(_ReaderBase):
def __init__(self):
super().__init__()
self.nlp = spacy.load('en_core_web_sm')
self.db = MultiWozDB(cfg.dbs)
self.vocab_size = self._build_vocab()
self.domain_files = json.loads(open(cfg.domain_file_path, 'r').read())
self.slot_value_set = json.loads(
open(cfg.slot_value_set_path, 'r').read())
if cfg.multi_acts_training:
self.multi_acts = json.loads(open(cfg.multi_acts_path, 'r').read())
self.gating_dict = {'ptr': 0, 'dontcare': 1, 'none': 2}
test_list = [
l.strip().lower() for l in open(cfg.test_list, 'r').readlines()
]
dev_list = [
l.strip().lower() for l in open(cfg.dev_list, 'r').readlines()
]
self.dev_files, self.test_files = {}, {}
for fn in test_list:
self.test_files[fn.replace('.json', '')] = 1
for fn in dev_list:
self.dev_files[fn.replace('.json', '')] = 1
self.exp_files = {}
if 'all' not in cfg.exp_domains:
for domain in cfg.exp_domains:
fn_list = self.domain_files.get(domain)
if not fn_list:
raise ValueError('[%s] is an invalid experiment setting' %
domain)
for fn in fn_list:
self.exp_files[fn.replace('.json', '')] = 1
self._load_data()
if cfg.limit_bspn_vocab:
self.bspn_masks = self._construct_bspn_constraint()
if cfg.limit_aspn_vocab:
self.aspn_masks = self._construct_aspn_constraint()
self.multi_acts_record = None
def _build_vocab(self):
self.vocab = utils.Vocab(cfg.vocab_size)
vp = cfg.vocab_path_train if cfg.mode == 'train' or cfg.vocab_path_eval is None else cfg.vocab_path_eval
# vp = cfg.vocab_path+'.json.freq.json'
self.vocab.load_vocab(vp)
return self.vocab.vocab_size
def _construct_bspn_constraint(self):
bspn_masks = {}
valid_domains = [
'restaurant', 'hotel', 'attraction', 'train', 'taxi', 'hospital'
]
all_dom_codes = [
self.vocab.encode('[' + d + ']') for d in valid_domains
]
all_slot_codes = [self.vocab.encode(s) for s in ontology.all_slots]
bspn_masks[self.vocab.encode(
'<go_b>')] = all_dom_codes + [self.vocab.encode('<eos_b>'), 0]
bspn_masks[self.vocab.encode('<eos_b>')] = [self.vocab.encode('<pad>')]
bspn_masks[self.vocab.encode('<pad>')] = [self.vocab.encode('<pad>')]
for domain, slot_values in self.slot_value_set.items():
if domain == 'police':
continue
dom_code = self.vocab.encode('[' + domain + ']')
bspn_masks[dom_code] = []
for slot, values in slot_values.items():
slot_code = self.vocab.encode(slot)
if slot_code not in bspn_masks:
bspn_masks[slot_code] = []
if slot_code not in bspn_masks[dom_code]:
bspn_masks[dom_code].append(slot_code)
for value in values:
for idx, v in enumerate(value.split()):
if not self.vocab.has_word(v):
continue
v_code = self.vocab.encode(v)
if v_code not in bspn_masks:
# print(self.vocab._word2idx)
bspn_masks[v_code] = []
if idx == 0 and v_code not in bspn_masks[slot_code]:
bspn_masks[slot_code].append(v_code)
if idx == (len(value.split()) - 1):
for w in all_dom_codes + all_slot_codes:
if self.vocab.encode(
'<eos_b>') not in bspn_masks[v_code]:
bspn_masks[v_code].append(
self.vocab.encode('<eos_b>'))
if w not in bspn_masks[v_code]:
bspn_masks[v_code].append(w)
break
if not self.vocab.has_word(value.split()[idx + 1]):
continue
next_v_code = self.vocab.encode(value.split()[idx + 1])
if next_v_code not in bspn_masks[v_code]:
bspn_masks[v_code].append(next_v_code)
bspn_masks[self.vocab.encode('<unk>')] = list(bspn_masks.keys())
with open('data/multi-woz-processed/bspn_masks.txt', 'w') as f:
for i, j in bspn_masks.items():
f.write(
self.vocab.decode(i) + ': ' +
' '.join([self.vocab.decode(int(m)) for m in j]) + '\n')
return bspn_masks
def _construct_aspn_constraint(self):
aspn_masks = {}
aspn_masks = {}
all_dom_codes = [
self.vocab.encode('[' + d + ']')
for d in ontology.dialog_acts.keys()
]
all_act_codes = [
self.vocab.encode('[' + a + ']')
for a in ontology.dialog_act_params
]
all_slot_codes = [
self.vocab.encode(s) for s in ontology.dialog_act_all_slots
]
aspn_masks[self.vocab.encode(
'<go_a>')] = all_dom_codes + [self.vocab.encode('<eos_a>'), 0]
aspn_masks[self.vocab.encode('<eos_a>')] = [self.vocab.encode('<pad>')]
aspn_masks[self.vocab.encode('<pad>')] = [self.vocab.encode('<pad>')]
# for d in all_dom_codes:
# aspn_masks[d] = all_act_codes
for a in all_act_codes:
aspn_masks[a] = all_dom_codes + all_slot_codes + [
self.vocab.encode('<eos_a>')
]
for domain, acts in ontology.dialog_acts.items():
dom_code = self.vocab.encode('[' + domain + ']')
aspn_masks[dom_code] = []
for a in acts:
act_code = self.vocab.encode('[' + a + ']')
if act_code not in aspn_masks[dom_code]:
aspn_masks[dom_code].append(act_code)
# for a, slots in ontology.dialog_act_params.items():
# act_code = self.vocab.encode('['+a+']')
# slot_codes = [self.vocab.encode(s) for s in slots]
# aspn_masks[act_code] = all_dom_codes + slot_codes + [self.vocab.encode('<eos_a>')]
for s in all_slot_codes:
aspn_masks[s] = all_dom_codes + all_slot_codes + [
self.vocab.encode('<eos_a>')
]
aspn_masks[self.vocab.encode('<unk>')] = list(aspn_masks.keys())
with open('data/multi-woz-processed/aspn_masks.txt', 'w') as f:
for i, j in aspn_masks.items():
f.write(
self.vocab.decode(i) + ': ' +
' '.join([self.vocab.decode(int(m)) for m in j]) + '\n')
return aspn_masks
def _load_data(self, save_temp=False):
self.data = json.loads(
open(cfg.data_path + cfg.data_file, 'r',
encoding='utf-8').read().lower())
self.train, self.dev, self.test = [], [], []
for fn, dial in self.data.items():
if 'all' in cfg.exp_domains or self.exp_files.get(fn):
if self.dev_files.get(fn):
self.dev.append(self._get_encoded_data(fn, dial))
elif self.test_files.get(fn):
self.test.append(self._get_encoded_data(fn, dial))
else:
self.train.append(self._get_encoded_data(fn, dial))
if save_temp:
json.dump(self.test,
open('data/multi-woz-analysis/test.encoded.json', 'w'),
indent=2)
self.vocab.save_vocab('data/multi-woz-analysis/vocab_temp')
random.shuffle(self.train)
random.shuffle(self.dev)
random.shuffle(self.test)
def _get_encoded_data(self, fn, dial):
encoded_dial = []
for idx, t in enumerate(dial['log']):
enc = {}
enc['dial_id'] = fn
enc['user'] = self.vocab.sentence_encode(t['user'].split() +
['<eos_u>'])
enc['usdx'] = self.vocab.sentence_encode(t['user_delex'].split() +
['<eos_u>'])
enc['resp'] = self.vocab.sentence_encode(t['resp'].split() +
['<eos_r>'])
enc['bspn'] = self.vocab.sentence_encode(t['constraint'].split() +
['<eos_b>'])
enc['bsdx'] = self.vocab.sentence_encode(t['cons_delex'].split() +
['<eos_b>'])
enc['aspn'] = self.vocab.sentence_encode(t['sys_act'].split() +
['<eos_a>'])
enc['dspn'] = self.vocab.sentence_encode(t['turn_domain'].split() +
['<eos_d>'])
enc['pointer'] = [int(i) for i in t['pointer'].split(',')]
enc['turn_domain'] = t['turn_domain'].split()
enc['turn_num'] = t['turn_num']
# TRADE labels
enc["gating_label"] = [
self.gating_dict[label]
for label in t["gating_label"].split(",")
]
enc["ptr_label"] = []
for labels in t["ptr_label"].split(","):
temp = []
for label in labels.split():
label_idx = self.vocab.encode(label)
if label_idx >= self.vocab.vocab_size:
temp.append(self.vocab.encode("<unk>"))
else:
temp.append(label_idx)
temp.append(self.vocab.encode("<eos_b>"))
enc["ptr_label"].append(temp)
if cfg.multi_acts_training:
enc['aspn_aug'] = []
if fn in self.multi_acts:
turn_ma = self.multi_acts[fn].get(str(idx), {})
for act_type, act_spans in turn_ma.items():
enc['aspn_aug'].append([
self.vocab.sentence_encode(a.split() + ['<eos_a>'])
for a in act_spans
])
encoded_dial.append(enc)
return encoded_dial
def bspan_to_constraint_dict(self, bspan, bspn_mode='bspn'):
bspan = bspan.split() if isinstance(bspan, str) else bspan
constraint_dict = {}
domain = None
conslen = len(bspan)
for idx, cons in enumerate(bspan):
cons = self.vocab.decode(cons) if type(cons) is not str else cons
if cons == '<eos_b>':
break
if '[' in cons:
if cons[1:-1] not in ontology.all_domains:
continue
domain = cons[1:-1]
elif cons in ontology.get_slot:
if domain is None:
continue
if cons == 'people':
# handle confusion of value name "people's portraits..." and slot people
try:
ns = bspan[idx + 1]
ns = self.vocab.decode(ns) if type(
ns) is not str else ns
if ns == "'s":
continue
except:
continue
if not constraint_dict.get(domain):
constraint_dict[domain] = {}
if bspn_mode == 'bsdx':
constraint_dict[domain][cons] = 1
continue
vidx = idx + 1
if vidx == conslen:
break
vt_collect = []
vt = bspan[vidx]
vt = self.vocab.decode(vt) if type(vt) is not str else vt
while vidx < conslen and vt != '<eos_b>' and '[' not in vt and vt not in ontology.get_slot:
vt_collect.append(vt)
vidx += 1
if vidx == conslen:
break
vt = bspan[vidx]
vt = self.vocab.decode(vt) if type(vt) is not str else vt
if vt_collect:
constraint_dict[domain][cons] = ' '.join(vt_collect)
return constraint_dict
def bspan_to_DBpointer(self, bspan, turn_domain):
constraint_dict = self.bspan_to_constraint_dict(bspan)
# print(constraint_dict)
matnums = self.db.get_match_num(constraint_dict)
match_dom = turn_domain[0] if len(turn_domain) == 1 else turn_domain[1]
match_dom = match_dom[1:-1] if match_dom.startswith('[') else match_dom
match = matnums[match_dom]
vector = self.db.addDBPointer(match_dom, match)
return vector
def aspan_to_act_list(self, aspan):
aspan = aspan.split() if isinstance(aspan, str) else aspan
acts = []
domain = None
conslen = len(aspan)
for idx, cons in enumerate(aspan):
cons = self.vocab.decode(cons) if type(cons) is not str else cons
if cons == '<eos_a>':
break
if '[' in cons and cons[1:-1] in ontology.dialog_acts:
domain = cons[1:-1]
elif '[' in cons and cons[1:-1] in ontology.dialog_act_params:
if domain is None:
continue
vidx = idx + 1
if vidx == conslen:
acts.append(domain + '-' + cons[1:-1] + '-none')
break
vt = aspan[vidx]
vt = self.vocab.decode(vt) if type(vt) is not str else vt
no_param_act = True
while vidx < conslen and vt != '<eos_a>' and '[' not in vt:
no_param_act = False
acts.append(domain + '-' + cons[1:-1] + '-' + vt)
vidx += 1
if vidx == conslen:
break
vt = aspan[vidx]
vt = self.vocab.decode(vt) if type(vt) is not str else vt
if no_param_act:
acts.append(domain + '-' + cons[1:-1] + '-none')
return acts
def dspan_to_domain(self, dspan):
domains = {}
dspan = dspan.split() if isinstance(dspan, str) else dspan
for d in dspan:
dom = self.vocab.decode(d) if type(d) is not str else d
if dom != '<eos_d>':
domains[dom] = 1
else:
break
return domains
def convert_batch(self, py_batch, py_prev, first_turn=False):
inputs = {}
if first_turn:
for item, py_list in py_prev.items():
batch_size = len(py_batch['user'])
inputs[item + '_np'] = np.array([[1]] * batch_size)
inputs[item + '_unk_np'] = np.array([[1]] * batch_size)
else:
for item, py_list in py_prev.items():
if py_list is None:
continue
if not cfg.enable_aspn and 'aspn' in item:
continue
if not cfg.enable_bspn and 'bspn' in item:
continue
if not cfg.enable_dspn and 'dspn' in item:
continue
prev_np = utils.padSeqs(py_list,
truncated=cfg.truncated,
trunc_method='pre')
inputs[item + '_np'] = prev_np
if item in ['pv_resp', 'pv_bspn', "pv_aspn"]:
inputs[item + '_unk_np'] = deepcopy(inputs[item + '_np'])
inputs[item + '_unk_np'][inputs[item + '_unk_np'] >=
self.vocab_size] = 2 # <unk>
else:
inputs[item + '_unk_np'] = inputs[item + '_np']
for item in ['user', 'usdx', 'resp', 'bspn', 'aspn', 'bsdx', 'dspn']:
if not cfg.enable_aspn and item == 'aspn':
continue
if not cfg.enable_bspn and item == 'bspn':
continue
if not cfg.enable_dspn and item == 'dspn':
continue
py_list = py_batch[item]
trunc_method = 'post' if item == 'resp' else 'pre'
# max_length = cfg.max_nl_length if item in ['user', 'usdx', 'resp'] else cfg.max_span_length
inputs[item + '_np'] = utils.padSeqs(py_list,
truncated=cfg.truncated,
trunc_method=trunc_method)
if item in ['user', 'usdx', 'resp', 'bspn', "aspn"]:
inputs[item + '_unk_np'] = deepcopy(inputs[item + '_np'])
inputs[item + '_unk_np'][
inputs[item + '_unk_np'] >= self.vocab_size] = 2 # <unk>
else:
inputs[item + '_unk_np'] = inputs[item + '_np']
if cfg.multi_acts_training and cfg.mode == 'train':
inputs['aspn_bidx'], multi_aspn = [], []
for bidx, aspn_type_list in enumerate(py_batch['aspn_aug']):
if aspn_type_list:
for aspn_list in aspn_type_list:
random.shuffle(aspn_list)
aspn = aspn_list[
0] #choose one random act span in each act type
multi_aspn.append(aspn)
inputs['aspn_bidx'].append(bidx)
if cfg.multi_act_sampling_num > 1:
for i in range(cfg.multi_act_sampling_num):
if len(aspn_list) >= i + 2:
aspn = aspn_list[
i +
1] #choose one random act span in each act type
multi_aspn.append(aspn)
inputs['aspn_bidx'].append(bidx)
if multi_aspn:
inputs['aspn_aug_np'] = utils.padSeqs(multi_aspn,
truncated=cfg.truncated,
trunc_method='pre')
inputs['aspn_aug_unk_np'] = inputs[
'aspn_aug_np'] # [all available aspn num in the batch, T]
inputs['db_np'] = np.array(py_batch['pointer'])
inputs['turn_domain'] = py_batch['turn_domain']
# TRADE
inputs["gating_label_np"] = np.array(py_batch["gating_label"])
batch_size = len(py_batch["ptr_label"])
slot_num = inputs["gating_label_np"].shape[1]
ptr_max_len = max(
[len(label) for batch in py_batch["ptr_label"] for label in batch])
inputs["ptr_label_np"] = np.zeros((batch_size, slot_num, ptr_max_len))
for bidx, batch in enumerate(py_batch["ptr_label"]):
for idx, label in enumerate(batch):
label_len = len(label)
inputs["ptr_label_np"][bidx, idx, :label_len] = np.array(label)
return inputs
def wrap_result(self, result_dict, eos_syntax=None):
decode_fn = self.vocab.sentence_decode
results = []
eos_syntax = ontology.eos_tokens if not eos_syntax else eos_syntax
gating_dict = {'ptr': 0, 'dontcare': 1, 'none': 2}
slot_list = [
'hotel-pricerange', 'hotel-type', 'hotel-parking', 'hotel-stay', 'hotel-day', 'hotel-people', \
'hotel-area', 'hotel-stars', 'hotel-internet', 'train-destination', 'train-day', 'train-departure', 'train-arriveby', \
'train-people', 'train-leaveat', 'attraction-area', 'restaurant-food', 'restaurant-pricerange', 'restaurant-area', \
'attraction-name', 'restaurant-name', 'attraction-type', 'hotel-name', 'taxi-leaveat', 'taxi-destination', 'taxi-departure', \
'restaurant-time', 'restaurant-day', 'restaurant-people', 'taxi-arriveby', "hospital-department"
]
if cfg.enable_trade:
field = [
'dial_id', 'turn_num', 'user', 'bspn_gen', 'bspn', 'resp_gen',
'resp', 'aspn_gen', 'aspn', 'dspn_gen', 'dspn', 'pointer',
"gating_label", "trade_gate", "ptr_label", "trade_ptr"
]
elif cfg.bspn_mode == 'bspn':
field = [
'dial_id', 'turn_num', 'user', 'bspn_gen', 'bspn', 'resp_gen',
'resp', 'aspn_gen', 'aspn', 'dspn_gen', 'dspn', 'pointer'
]
elif not cfg.enable_dst:
field = [
'dial_id', 'turn_num', 'user', 'bsdx_gen', 'bsdx', 'resp_gen',
'resp', 'aspn_gen', 'aspn', 'dspn_gen', 'dspn', 'bspn',
'pointer'
]
else:
field = [
'dial_id', 'turn_num', 'user', 'bsdx_gen', 'bsdx', 'resp_gen',
'resp', 'aspn_gen', 'aspn', 'dspn_gen', 'dspn', 'bspn_gen',
'bspn', 'pointer'
]
if self.multi_acts_record is not None:
field.insert(7, 'multi_act_gen')
for dial_id, turns in result_dict.items():
entry = {'dial_id': dial_id, 'turn_num': len(turns)}
for prop in field[2:]:
entry[prop] = ''
results.append(entry) # info of dialogue
for turn_no, turn in enumerate(turns):
entry = {'dial_id': dial_id}
trade_gate = []
for key in field:
if key in ['dial_id']:
continue
v = turn.get(key, '')
if key == "trade_gate":
trade_gate = v
entry[key] = v
continue
elif key == "trade_ptr":
trade_ptr = []
for idx, slot in enumerate(v):
if trade_gate[idx].item() == 0:
trade_ptr.append(
decode_fn(slot.tolist(),
eos=eos_syntax["trade"]))
elif trade_gate[idx].item() == 1:
trade_ptr.append("do n't care")
else:
trade_ptr.append("none")
entry[key] = trade_ptr
continue
elif key == "ptr_label":
ptr_label = []
for slot in v:
ptr_label.append(
decode_fn(slot, eos=eos_syntax["trade"]))
entry[key] = ptr_label
continue
if key == 'turn_domain':
v = ' '.join(v)
entry[key] = decode_fn(
v, eos=eos_syntax[key]
) if key in eos_syntax and v != '' else v
results.append(entry)
return results, field
def restore(self, resp, domain, constraint_dict, mat_ents):
restored = resp
restored = restored.replace('[value_reference]', '53022')
restored = restored.replace('[value_car]', 'BMW')
# restored.replace('[value_phone]', '830-430-6666')
for d in domain:
constraint = constraint_dict.get(d, None)
if constraint:
if 'stay' in constraint:
restored = restored.replace('[value_stay]',
constraint['stay'])
if 'day' in constraint:
restored = restored.replace('[value_day]',
constraint['day'])
if 'people' in constraint:
restored = restored.replace('[value_people]',
constraint['people'])
if 'time' in constraint:
restored = restored.replace('[value_time]',
constraint['time'])
if 'type' in constraint:
restored = restored.replace('[value_type]',
constraint['type'])
if d in mat_ents and len(mat_ents[d]) == 0:
for s in constraint:
if s == 'pricerange' and d in [
'hotel', 'restaurant'
] and 'price]' in restored:
restored = restored.replace(
'[value_price]', constraint['pricerange'])
if s + ']' in restored:
restored = restored.replace(
'[value_%s]' % s, constraint[s])
if '[value_choice' in restored and mat_ents.get(d):
restored = restored.replace('[value_choice]',
str(len(mat_ents[d])))
if '[value_choice' in restored:
restored = restored.replace('[value_choice]', '3')
# restored.replace('[value_car]', 'BMW')
try:
ent = mat_ents.get(domain[-1], [])
if ent:
ent = ent[0]
for t in restored.split():
if '[value' in t:
slot = t[7:-1]
if ent.get(slot):
if domain[-1] == 'hotel' and slot == 'price':
slot = 'pricerange'
restored = restored.replace(t, ent[slot])
elif slot == 'price':
if ent.get('pricerange'):
restored = restored.replace(
t, ent['pricerange'])
else:
print(restored, domain)
except:
print(resp)
print(restored)
quit()
restored = restored.replace('[value_phone]', '62781111')
restored = restored.replace('[value_postcode]', 'CG9566')
restored = restored.replace('[value_address]', 'Parkside, Cambridge')
# if '[value_' in restored:
# print(domain)
# # print(mat_ents)
# print(resp)
# print(restored)
return restored
def record_utterance(self, result_dict):
decode_fn = self.vocab.sentence_decode
ordered_dial = {}
for dial_id, turns in result_dict.items():
diverse = 0
turn_count = 0
for turn_no, turn in enumerate(turns):
act_collect = {}
act_type_collect = {}
slot_score = 0
for i in range(cfg.nbest):
aspn = decode_fn(turn['multi_act'][i],
eos=ontology.eos_tokens['aspn'])
pred_acts = self.aspan_to_act_list(' '.join(aspn))
act_type = ''
for act in pred_acts:
d, a, s = act.split('-')
if d + '-' + a not in act_collect:
act_collect[d + '-' + a] = {s: 1}
slot_score += 1
act_type += d + '-' + a + ';'
elif s not in act_collect:
act_collect[d + '-' + a][s] = 1
slot_score += 1
act_type_collect[act_type] = 1
turn_count += 1
diverse += len(act_collect) * 3 + slot_score
ordered_dial[dial_id] = diverse / turn_count
ordered_dial = sorted(ordered_dial.keys(),
key=lambda x: -ordered_dial[x])
dialog_record = {}
with open(cfg.eval_load_path + '/dialogue_record.csv', 'w') as rf:
writer = csv.writer(rf)
for dial_id in ordered_dial:
dialog_record[dial_id] = []
turns = result_dict[dial_id]
writer.writerow([dial_id])
for turn_no, turn in enumerate(turns):
user = decode_fn(turn['user'],
eos=ontology.eos_tokens['user'])
bspn = decode_fn(turn['bspn'],
eos=ontology.eos_tokens['bspn'])
aspn = decode_fn(turn['aspn'],
eos=ontology.eos_tokens['aspn'])
resp = decode_fn(turn['resp'],
eos=ontology.eos_tokens['resp'])
constraint_dict = self.bspan_to_constraint_dict(bspn)
# print(constraint_dict)
mat_ents = self.db.get_match_num(constraint_dict, True)
domain = [
i[1:-1]
for i in self.dspan_to_domain(turn['dspn']).keys()
]
restored = self.restore(resp, domain, constraint_dict,
mat_ents)
writer.writerow([
turn_no, user, turn['pointer'], domain, restored, resp
])
turn_record = {
'user': user,
'bspn': bspn,
'aspn': aspn,
'dom': domain,
'resp': resp,
'resp_res': restored
}
resp_col = []
aspn_col = []
resp_restore_col = []
for i in range(cfg.nbest):
aspn = decode_fn(turn['multi_act'][i],
eos=ontology.eos_tokens['aspn'])
resp = decode_fn(turn['multi_resp'][i],
eos=ontology.eos_tokens['resp'])
restored = self.restore(resp, domain, constraint_dict,
mat_ents)
resp_col.append(resp)
resp_restore_col.append(restored)
aspn_col.append(aspn)
zipped = list(zip(resp_restore_col, resp_col, aspn_col))
zipped.sort(key=lambda s: len(s[0]))
resp_restore_col = list(list(zip(*zipped))[0])
aspn_col = list(list(zip(*zipped))[2])
resp_col = list(list(zip(*zipped))[1])
turn_record['aspn_col'] = aspn_col
turn_record['resp_col'] = resp_col
turn_record['resp_res_col'] = resp_restore_col
for i in range(cfg.nbest):
# aspn = decode_fn(turn['multi_act'][i], eos=ontology.eos_tokens['aspn'])
resp = resp_col[i]
aspn = aspn_col[i]
resp_restore = resp_restore_col[i]
writer.writerow(['', resp_restore, resp, aspn])
dialog_record[dial_id].append(turn_record)
class DataPreprocessor(object):
def __init__(self):
self.nlp = spacy.load('en_core_web_sm')
self.db = MultiWozDB(cfg.dbs)
data_path = 'data/multi-woz/annotated_user_da_with_span_full.json'
archive = zipfile.ZipFile(data_path + '.zip', 'r')
self.convlab_data = json.loads(
archive.open(data_path.split('/')[-1],
'r').read().decode('utf-8').lower())
self.delex_sg_valdict_path = 'data/multi-woz-processed/delex_single_valdict.json'
self.delex_mt_valdict_path = 'data/multi-woz-processed/delex_multi_valdict.json'
self.ambiguous_val_path = 'data/multi-woz-processed/ambiguous_values.json'
self.delex_refs_path = 'data/multi-woz-processed/reference_no.json'
self.delex_refs = json.loads(open(self.delex_refs_path, 'r').read())
if not os.path.exists(self.delex_sg_valdict_path):
self.delex_sg_valdict, self.delex_mt_valdict, self.ambiguous_vals = self.get_delex_valdict(
)
else:
self.delex_sg_valdict = json.loads(
open(self.delex_sg_valdict_path, 'r').read())
self.delex_mt_valdict = json.loads(
open(self.delex_mt_valdict_path, 'r').read())
self.ambiguous_vals = json.loads(
open(self.ambiguous_val_path, 'r').read())
self.vocab = utils.Vocab(cfg.vocab_size)
def delex_by_annotation(self, dial_turn):
u = dial_turn['text'].split()
span = dial_turn['span_info']
for s in span:
slot = s[1]
if slot == 'open':
continue
if ontology.da_abbr_to_slot_name.get(slot):
slot = ontology.da_abbr_to_slot_name[slot]
for idx in range(s[3], s[4] + 1):
u[idx] = ''
try:
u[s[3]] = '[value_' + slot + ']'
except:
u[5] = '[value_' + slot + ']'
u_delex = ' '.join([t for t in u if t is not ''])
u_delex = u_delex.replace(
'[value_address] , [value_address] , [value_address]',
'[value_address]')
u_delex = u_delex.replace('[value_address] , [value_address]',
'[value_address]')
u_delex = u_delex.replace('[value_name] [value_name]', '[value_name]')
u_delex = u_delex.replace('[value_name]([value_phone] )',
'[value_name] ( [value_phone] )')
return u_delex
def delex_by_valdict(self, text):
text = clean_text(text)
text = re.sub(r'\d{5}\s?\d{5,7}', '[value_phone]', text)
text = re.sub(r'\d[\s-]stars?', '[value_stars]', text)
text = re.sub(r'\$\d+|\$?\d+.?(\d+)?\s(pounds?|gbps?)',
'[value_price]', text)
text = re.sub(r'tr[\d]{4}', '[value_id]', text)
text = re.sub(
r'([a-z]{1}[\. ]?[a-z]{1}[\. ]?\d{1,2}[, ]+\d{1}[\. ]?[a-z]{1}[\. ]?[a-z]{1}|[a-z]{2}\d{2}[a-z]{2})',
'[value_postcode]', text)
for value, slot in self.delex_mt_valdict.items():
text = text.replace(value, '[value_%s]' % slot)
for value, slot in self.delex_sg_valdict.items():
tokens = text.split()
for idx, tk in enumerate(tokens):
if tk == value:
tokens[idx] = '[value_%s]' % slot
text = ' '.join(tokens)
for ambg_ent in self.ambiguous_vals:
start_idx = text.find(
' ' + ambg_ent
) # ely is a place, but appears in words like moderately
if start_idx == -1:
continue
front_words = text[:start_idx].split()
ent_type = 'time' if ':' in ambg_ent else 'place'
for fw in front_words[::-1]:
if fw in [
'arrive', 'arrives', 'arrived', 'arriving', 'arrival',
'destination', 'there', 'reach', 'to', 'by', 'before'
]:
slot = '[value_arrive]' if ent_type == 'time' else '[value_destination]'
text = re.sub(' ' + ambg_ent, ' ' + slot, text)
elif fw in [
'leave', 'leaves', 'leaving', 'depart', 'departs',
'departing', 'departure', 'from', 'after', 'pulls'
]:
slot = '[value_leave]' if ent_type == 'time' else '[value_departure]'
text = re.sub(' ' + ambg_ent, ' ' + slot, text)
text = text.replace('[value_car] [value_car]', '[value_car]')
return text
def get_delex_valdict(self, ):
skip_entry_type = {
'taxi': ['taxi_phone'],
'police': ['id'],
'hospital': ['id'],
'hotel': [
'id', 'location', 'internet', 'parking', 'takesbookings',
'stars', 'price', 'n', 'postcode', 'phone'
],
'attraction': [
'id', 'location', 'pricerange', 'price', 'openhours',
'postcode', 'phone'
],
'train': ['price', 'id'],
'restaurant': [
'id', 'location', 'introduction', 'signature', 'type',
'postcode', 'phone'
],
}
entity_value_to_slot = {}
ambiguous_entities = []
for domain, db_data in self.db.dbs.items():
print('Processing entity values in [%s]' % domain)
if domain != 'taxi':
for db_entry in db_data:
for slot, value in db_entry.items():
if slot not in skip_entry_type[domain]:
if type(value) is not str:
raise TypeError(
"value '%s' in domain '%s' should be rechecked"
% (slot, domain))
else:
slot, value = clean_slot_values(
domain, slot, value)
value = ' '.join([
token.text for token in self.nlp(value)
]).strip()
if value in entity_value_to_slot and entity_value_to_slot[
value] != slot:
# print(value, ": ",entity_value_to_slot[value], slot)
ambiguous_entities.append(value)
entity_value_to_slot[value] = slot
else: # taxi db specific
db_entry = db_data[0]
for slot, ent_list in db_entry.items():
if slot not in skip_entry_type[domain]:
for ent in ent_list:
entity_value_to_slot[ent] = 'car'
ambiguous_entities = set(ambiguous_entities)
ambiguous_entities.remove('cambridge')
ambiguous_entities = list(ambiguous_entities)
for amb_ent in ambiguous_entities: # departure or destination? arrive time or leave time?
entity_value_to_slot.pop(amb_ent)
entity_value_to_slot['parkside'] = 'address'
entity_value_to_slot['parkside, cambridge'] = 'address'
entity_value_to_slot['cambridge belfry'] = 'name'
entity_value_to_slot['hills road'] = 'address'
entity_value_to_slot['hills rd'] = 'address'
entity_value_to_slot['Parkside Police Station'] = 'name'
single_token_values = {}
multi_token_values = {}
for val, slt in entity_value_to_slot.items():
if val in ['cambridge']:
continue
if len(val.split()) > 1:
multi_token_values[val] = slt
else:
single_token_values[val] = slt
with open(self.delex_sg_valdict_path, 'w') as f:
single_token_values = OrderedDict(
sorted(single_token_values.items(),
key=lambda kv: len(kv[0]),
reverse=True))
json.dump(single_token_values, f, indent=2)
print('single delex value dict saved!')
with open(self.delex_mt_valdict_path, 'w') as f:
multi_token_values = OrderedDict(
sorted(multi_token_values.items(),
key=lambda kv: len(kv[0]),
reverse=True))
json.dump(multi_token_values, f, indent=2)
print('multi delex value dict saved!')
with open(self.ambiguous_val_path, 'w') as f:
json.dump(ambiguous_entities, f, indent=2)
print('ambiguous value dict saved!')
return single_token_values, multi_token_values, ambiguous_entities
def preprocess_main(self, save_path=None, is_test=False):
"""
"""
data = {}
count = 0
self.unique_da = {}
ordered_sysact_dict = {}
for fn, raw_dial in tqdm(list(self.convlab_data.items())):
count += 1
# if count == 100:
# break
compressed_goal = {}
dial_domains, dial_reqs = [], []
for dom, g in raw_dial['goal'].items():
if dom != 'topic' and dom != 'message' and g:
if g.get('reqt'):
for i, req_slot in enumerate(g['reqt']):
if ontology.normlize_slot_names.get(req_slot):
g['reqt'][i] = ontology.normlize_slot_names[
req_slot]
dial_reqs.append(g['reqt'][i])
compressed_goal[dom] = g
if dom in ontology.all_domains:
dial_domains.append(dom)
dial_reqs = list(set(dial_reqs))
dial = {'goal': compressed_goal, 'log': []}
single_turn = {}
constraint_dict = OrderedDict()
prev_constraint_dict = {}
prev_turn_domain = ['general']
ordered_sysact_dict[fn] = {}
for turn_num, dial_turn in enumerate(raw_dial['log']):
dial_state = dial_turn['metadata']
if not dial_state: # user
u = ' '.join(clean_text(dial_turn['text']).split())
if dial_turn['span_info']:
u_delex = clean_text(
self.delex_by_annotation(dial_turn))
else:
u_delex = self.delex_by_valdict(dial_turn['text'])
single_turn['user'] = u
single_turn['user_delex'] = u_delex
else: #system
if dial_turn['span_info']:
s_delex = clean_text(
self.delex_by_annotation(dial_turn))
else:
if not dial_turn['text']:
print(fn)
s_delex = self.delex_by_valdict(dial_turn['text'])
single_turn['resp'] = s_delex
# get belief state
for domain in dial_domains:
if not constraint_dict.get(domain):
constraint_dict[domain] = OrderedDict()
info_sv = dial_state[domain]['semi']
for s, v in info_sv.items():
s, v = clean_slot_values(domain, s, v)
if len(v.split()) > 1:
v = ' '.join([
token.text for token in self.nlp(v)
]).strip()
if v != '':
constraint_dict[domain][s] = v
book_sv = dial_state[domain]['book']
for s, v in book_sv.items():
if s == 'booked':
continue
s, v = clean_slot_values(domain, s, v)
if len(v.split()) > 1:
v = ' '.join([
token.text for token in self.nlp(v)
]).strip()
if v != '':
constraint_dict[domain][s] = v
constraints = []
cons_delex = []
turn_dom_bs = []
for domain, info_slots in constraint_dict.items():
if info_slots:
constraints.append('[' + domain + ']')
cons_delex.append('[' + domain + ']')
for slot, value in info_slots.items():
constraints.append(slot)
constraints.extend(value.split())
cons_delex.append(slot)
if domain not in prev_constraint_dict:
turn_dom_bs.append(domain)
elif prev_constraint_dict[
domain] != constraint_dict[domain]:
turn_dom_bs.append(domain)
sys_act_dict = {}
turn_dom_da = set()
for act in dial_turn['dialog_act']:
d, a = act.split('-')
turn_dom_da.add(d)
turn_dom_da = list(turn_dom_da)
if len(turn_dom_da) != 1 and 'general' in turn_dom_da:
turn_dom_da.remove('general')
if len(turn_dom_da) != 1 and 'booking' in turn_dom_da:
turn_dom_da.remove('booking')
# get turn domain
turn_domain = turn_dom_bs
for dom in turn_dom_da:
if dom != 'booking' and dom not in turn_domain:
turn_domain.append(dom)
if not turn_domain:
turn_domain = prev_turn_domain
if len(turn_domain) == 2 and 'general' in turn_domain:
turn_domain.remove('general')
if len(turn_domain) == 2:
if len(prev_turn_domain) == 1 and prev_turn_domain[
0] == turn_domain[1]:
turn_domain = turn_domain[::-1]
# get system action
for dom in turn_domain:
sys_act_dict[dom] = {}
add_to_last_collect = []
booking_act_map = {
'inform': 'offerbook',
'book': 'offerbooked'
}
for act, params in dial_turn['dialog_act'].items():
if act == 'general-greet':
continue
d, a = act.split('-')
if d == 'general' and d not in sys_act_dict:
sys_act_dict[d] = {}
if d == 'booking':
d = turn_domain[0]
a = booking_act_map.get(a, a)
add_p = []
for param in params:
p = param[0]
if p == 'none':
continue
elif ontology.da_abbr_to_slot_name.get(p):
p = ontology.da_abbr_to_slot_name[p]
if p not in add_p:
add_p.append(p)
add_to_last = True if a in [
'request', 'reqmore', 'bye', 'offerbook'
] else False
if add_to_last:
add_to_last_collect.append((d, a, add_p))
else:
sys_act_dict[d][a] = add_p
for d, a, add_p in add_to_last_collect:
sys_act_dict[d][a] = add_p
for d in copy.copy(sys_act_dict):
acts = sys_act_dict[d]
if not acts:
del sys_act_dict[d]
if 'inform' in acts and 'offerbooked' in acts:
for s in sys_act_dict[d]['inform']:
sys_act_dict[d]['offerbooked'].append(s)
del sys_act_dict[d]['inform']
ordered_sysact_dict[fn][len(dial['log'])] = sys_act_dict
sys_act = []
if 'general-greet' in dial_turn['dialog_act']:
sys_act.extend(['[general]', '[greet]'])
for d, acts in sys_act_dict.items():
sys_act += ['[' + d + ']']
for a, slots in acts.items():
self.unique_da[d + '-' + a] = 1
sys_act += ['[' + a + ']']
sys_act += slots
# get db pointers
matnums = self.db.get_match_num(constraint_dict)
match_dom = turn_domain[0] if len(
turn_domain) == 1 else turn_domain[1]
match = matnums[match_dom]
dbvec = self.db.addDBPointer(match_dom, match)
bkvec = self.db.addBookingPointer(dial_turn['dialog_act'])
single_turn['pointer'] = ','.join(
[str(d) for d in dbvec + bkvec])
single_turn['match'] = str(match)
single_turn['constraint'] = ' '.join(constraints)
single_turn['cons_delex'] = ' '.join(cons_delex)
single_turn['sys_act'] = ' '.join(sys_act)
single_turn['turn_num'] = len(dial['log'])
single_turn['turn_domain'] = ' '.join(
['[' + d + ']' for d in turn_domain])
prev_turn_domain = copy.deepcopy(turn_domain)
prev_constraint_dict = copy.deepcopy(constraint_dict)
if 'user' in single_turn:
dial['log'].append(single_turn)
for t in single_turn['user'].split() + single_turn[
'resp'].split() + constraints + sys_act:
self.vocab.add_word(t)
for t in single_turn['user_delex'].split():
if '[' in t and ']' in t and not t.startswith(
'[') and not t.endswith(']'):
single_turn['user_delex'].replace(
t, t[t.index('['):t.index(']') + 1])
elif not self.vocab.has_word(t):
self.vocab.add_word(t)
single_turn = {}
data[fn] = dial
# pprint(dial)
# if count == 20:
# break
self.vocab.construct()
self.vocab.save_vocab('data/multi-woz-processed/vocab')
with open('data/multi-woz-analysis/dialog_acts.json', 'w') as f:
json.dump(ordered_sysact_dict, f, indent=2)
with open('data/multi-woz-analysis/dialog_act_type.json', 'w') as f:
json.dump(self.unique_da, f, indent=2)
return data
class MultiWozReader(_ReaderBase):
def __init__(self, vocab=None):
super().__init__()
self.nlp = spacy.load('en_core_web_sm')
self.db = MultiWozDB(cfg.dbs)
self.domain_files = json.loads(open(cfg.domain_file_path, 'r').read())
self.slot_value_set = json.loads(
open(cfg.slot_value_set_path, 'r').read())
if cfg.multi_acts_training:
self.multi_acts = json.loads(open(cfg.multi_acts_path, 'r').read())
test_list = [
l.strip().lower() for l in open(cfg.test_list, 'r').readlines()
]
dev_list = [
l.strip().lower() for l in open(cfg.dev_list, 'r').readlines()
]
self.dev_files, self.test_files = {}, {}
for fn in test_list:
self.test_files[fn.replace('.json', '')] = 1
for fn in dev_list:
self.dev_files[fn.replace('.json', '')] = 1
self.exp_files = {}
if 'all' not in cfg.exp_domains:
for domain in cfg.exp_domains:
fn_list = self.domain_files.get(domain)
if not fn_list:
raise ValueError('[%s] is an invalid experiment setting' %
domain)
for fn in fn_list:
self.exp_files[fn.replace('.json', '')] = 1
if vocab:
self.vocab = vocab
self.vocab_size = vocab.size
else:
self.vocab_size = self._build_vocab()
self._load_data()
if cfg.limit_bspn_vocab:
self.bspn_masks = self._construct_bspn_constraint()
if cfg.limit_aspn_vocab:
self.aspn_masks = self._construct_aspn_constraint()
self.multi_acts_record = None
def _build_vocab(self):
self.vocab = utils.Vocab(cfg.vocab_size)
vp = cfg.vocab_path_train if cfg.mode == 'train' or cfg.vocab_path_eval is None else cfg.vocab_path_eval
# vp = cfg.vocab_path+'.json.freq.json'
self.vocab.load_vocab(vp)
return self.vocab.vocab_size
def _construct_bspn_constraint(self):
bspn_masks = {}
valid_domains = [
'restaurant', 'hotel', 'attraction', 'train', 'taxi', 'hospital'
]
all_dom_codes = [
self.vocab.encode('[' + d + ']') for d in valid_domains
]
all_slot_codes = [self.vocab.encode(s) for s in ontology.all_slots]
bspn_masks[self.vocab.encode(
'<go_b>')] = all_dom_codes + [self.vocab.encode('<eos_b>'), 0]
bspn_masks[self.vocab.encode('<eos_b>')] = [self.vocab.encode('<pad>')]
bspn_masks[self.vocab.encode('<pad>')] = [self.vocab.encode('<pad>')]
for domain, slot_values in self.slot_value_set.items():
if domain == 'police':
continue
dom_code = self.vocab.encode('[' + domain + ']')
bspn_masks[dom_code] = []
for slot, values in slot_values.items():
slot_code = self.vocab.encode(slot)
if slot_code not in bspn_masks:
bspn_masks[slot_code] = []
if slot_code not in bspn_masks[dom_code]:
bspn_masks[dom_code].append(slot_code)
for value in values:
for idx, v in enumerate(value.split()):
if not self.vocab.has_word(v):
continue
v_code = self.vocab.encode(v)
if v_code not in bspn_masks:
# print(self.vocab._word2idx)
bspn_masks[v_code] = []
if idx == 0 and v_code not in bspn_masks[slot_code]:
bspn_masks[slot_code].append(v_code)
if idx == (len(value.split()) - 1):
for w in all_dom_codes + all_slot_codes:
if self.vocab.encode(
'<eos_b>') not in bspn_masks[v_code]:
bspn_masks[v_code].append(
self.vocab.encode('<eos_b>'))
if w not in bspn_masks[v_code]:
bspn_masks[v_code].append(w)
break
if not self.vocab.has_word(value.split()[idx + 1]):
continue
next_v_code = self.vocab.encode(value.split()[idx + 1])
if next_v_code not in bspn_masks[v_code]:
bspn_masks[v_code].append(next_v_code)
bspn_masks[self.vocab.encode('<unk>')] = list(bspn_masks.keys())
with open('data/multi-woz-processed/bspn_masks.txt', 'w') as f:
for i, j in bspn_masks.items():
f.write(
self.vocab.decode(i) + ': ' +
' '.join([self.vocab.decode(int(m)) for m in j]) + '\n')
return bspn_masks
def _construct_aspn_constraint(self):
aspn_masks = {}
aspn_masks = {}
all_dom_codes = [
self.vocab.encode('[' + d + ']')
for d in ontology.dialog_acts.keys()
]
all_act_codes = [
self.vocab.encode('[' + a + ']')
for a in ontology.dialog_act_params
]
all_slot_codes = [
self.vocab.encode(s) for s in ontology.dialog_act_all_slots
]
aspn_masks[self.vocab.encode(
'<go_a>')] = all_dom_codes + [self.vocab.encode('<eos_a>'), 0]
aspn_masks[self.vocab.encode('<eos_a>')] = [self.vocab.encode('<pad>')]
aspn_masks[self.vocab.encode('<pad>')] = [self.vocab.encode('<pad>')]
# for d in all_dom_codes:
# aspn_masks[d] = all_act_codes
for a in all_act_codes:
aspn_masks[a] = all_dom_codes + all_slot_codes + [
self.vocab.encode('<eos_a>')
]
for domain, acts in ontology.dialog_acts.items():
dom_code = self.vocab.encode('[' + domain + ']')
aspn_masks[dom_code] = []
for a in acts:
act_code = self.vocab.encode('[' + a + ']')
if act_code not in aspn_masks[dom_code]:
aspn_masks[dom_code].append(act_code)
# for a, slots in ontology.dialog_act_params.items():
# act_code = self.vocab.encode('['+a+']')
# slot_codes = [self.vocab.encode(s) for s in slots]
# aspn_masks[act_code] = all_dom_codes + slot_codes + [self.vocab.encode('<eos_a>')]
for s in all_slot_codes:
aspn_masks[s] = all_dom_codes + all_slot_codes + [
self.vocab.encode('<eos_a>')
]
aspn_masks[self.vocab.encode('<unk>')] = list(aspn_masks.keys())
with open('data/multi-woz-processed/aspn_masks.txt', 'w') as f:
for i, j in aspn_masks.items():
f.write(
self.vocab.decode(i) + ': ' +
' '.join([self.vocab.decode(int(m)) for m in j]) + '\n')
return aspn_masks
def _load_data(self, save_temp=False):
self.data = json.loads(
open(cfg.data_path + cfg.data_file, 'r',
encoding='utf-8').read().lower())
self.train, self.dev, self.test = [], [], []
# data_fraction = 0.05
# train_count = 0
for fn, dial in self.data.items():
#print(fn)
if 'all' in cfg.exp_domains or self.exp_files.get(fn):
if self.dev_files.get(fn):
self.dev.append(self._get_encoded_data(fn, dial))
elif self.test_files.get(fn):
self.test.append(self._get_encoded_data(fn, dial))
else:
# if train_count>round(data_fraction*8438):
# continue
self.train.append(self._get_encoded_data(fn, dial))
# train_count+=1
if save_temp:
json.dump(self.test,
open('data/multi-woz-analysis/test.encoded.json', 'w'),
indent=2)
self.vocab.save_vocab('data/multi-woz-analysis/vocab_temp')
random.shuffle(self.train)
random.shuffle(self.dev)
random.shuffle(self.test)
def _get_encoded_data(self, fn, dial):
encoded_dial = []
for idx, t in enumerate(dial['log']):
enc = {}
enc['dial_id'] = fn
enc['user'] = self.vocab.sentence_encode(t['user'].split() +
['<eos_u>'])
enc['usdx'] = self.vocab.sentence_encode(t['user_delex'].split() +
['<eos_u>'])
enc['resp'] = self.vocab.sentence_encode(t['resp'].split() +
['<eos_r>'])
enc['bspn'] = self.vocab.sentence_encode(t['constraint'].split() +
['<eos_b>'])
enc['bsdx'] = self.vocab.sentence_encode(t['cons_delex'].split() +
['<eos_b>'])
enc['aspn'] = self.vocab.sentence_encode(t['sys_act'].split() +
['<eos_a>'])
enc['dspn'] = self.vocab.sentence_encode(t['turn_domain'].split() +
['<eos_d>'])
enc['pointer'] = [int(i) for i in t['pointer'].split(',')]
enc['input_pointer'] = enc['pointer']
enc['turn_domain'] = t['turn_domain'].split()
enc['turn_num'] = t['turn_num']
if cfg.multi_acts_training:
enc['aspn_aug'] = []
if fn in self.multi_acts:
turn_ma = self.multi_acts[fn].get(str(idx), {})
for act_type, act_spans in turn_ma.items():
enc['aspn_aug'].append([
self.vocab.sentence_encode(a.split() + ['<eos_a>'])
for a in act_spans
])
encoded_dial.append(enc)
return encoded_dial
def bspan_to_constraint_dict(self, bspan, bspn_mode='bspn'):
bspan = bspan.split() if isinstance(bspan, str) else bspan
constraint_dict = {}
domain = None
conslen = len(bspan)
for idx, cons in enumerate(bspan):
cons = self.vocab.decode(cons) if type(cons) is not str else cons
if cons == '<eos_b>':
break
if '[' in cons:
if cons[1:-1] not in ontology.all_domains:
continue
domain = cons[1:-1]
elif cons in ontology.get_slot:
if domain is None:
continue
if cons == 'people':
# handle confusion of value name "people's portraits..." and slot people
try:
ns = bspan[idx + 1]
ns = self.vocab.decode(ns) if type(
ns) is not str else ns
if ns == "'s":
continue
except:
continue
if not constraint_dict.get(domain):
constraint_dict[domain] = {}
if bspn_mode == 'bsdx':
constraint_dict[domain][cons] = 1
continue
vidx = idx + 1
if vidx == conslen:
break
vt_collect = []
vt = bspan[vidx]
vt = self.vocab.decode(vt) if type(vt) is not str else vt
while vidx < conslen and vt != '<eos_b>' and '[' not in vt and vt not in ontology.get_slot:
vt_collect.append(vt)
vidx += 1
if vidx == conslen:
break
vt = bspan[vidx]
vt = self.vocab.decode(vt) if type(vt) is not str else vt
if vt_collect:
constraint_dict[domain][cons] = ' '.join(vt_collect)
return constraint_dict
def bspan_to_DBpointer(self, bspan, turn_domain):
constraint_dict = self.bspan_to_constraint_dict(bspan)
# print(constraint_dict)
matnums = self.db.get_match_num(constraint_dict)
match_dom = turn_domain[0] if len(turn_domain) == 1 else turn_domain[1]
match_dom = match_dom[1:-1] if match_dom.startswith('[') else match_dom
match = matnums[match_dom]
vector = self.db.addDBPointer(match_dom, match)
return vector
def aspan_to_act_list(self, aspan):
aspan = aspan.split() if isinstance(aspan, str) else aspan
acts = []
domain = None
conslen = len(aspan)
for idx, cons in enumerate(aspan):
cons = self.vocab.decode(cons) if type(cons) is not str else cons
if cons == '<eos_a>':
break
if '[' in cons and cons[1:-1] in ontology.dialog_acts:
domain = cons[1:-1]
elif '[' in cons and cons[1:-1] in ontology.dialog_act_params:
if domain is None:
continue
vidx = idx + 1
if vidx == conslen:
acts.append(domain + '-' + cons[1:-1] + '-none')
break
vt = aspan[vidx]
vt = self.vocab.decode(vt) if type(vt) is not str else vt
no_param_act = True
while vidx < conslen and vt != '<eos_a>' and '[' not in vt:
no_param_act = False
acts.append(domain + '-' + cons[1:-1] + '-' + vt)
vidx += 1
if vidx == conslen:
break
vt = aspan[vidx]
vt = self.vocab.decode(vt) if type(vt) is not str else vt
if no_param_act:
acts.append(domain + '-' + cons[1:-1] + '-none')
return acts
def dspan_to_domain(self, dspan):
domains = {}
dspan = dspan.split() if isinstance(dspan, str) else dspan
for d in dspan:
dom = self.vocab.decode(d) if type(d) is not str else d
if dom != '<eos_d>':
domains[dom] = 1
else:
break
return domains
def convert_batch(self, py_batch, py_prev, first_turn=False):
inputs = {}
if first_turn:
for item, py_list in py_prev.items():
batch_size = len(py_batch['user'])
inputs[item + '_np'] = np.array([[1]] * batch_size)
inputs[item + '_unk_np'] = np.array([[1]] * batch_size)
else:
for item, py_list in py_prev.items():
if py_list is None:
continue
if not cfg.enable_aspn and 'aspn' in item:
continue
if not cfg.enable_bspn and 'bspn' in item:
continue
if not cfg.enable_dspn and 'dspn' in item:
continue
prev_np = utils.padSeqs(py_list,
truncated=cfg.truncated,
trunc_method='pre')
inputs[item + '_np'] = prev_np
if item in ['pv_resp', 'pv_bspn']:
inputs[item + '_unk_np'] = deepcopy(inputs[item + '_np'])
inputs[item + '_unk_np'][inputs[item + '_unk_np'] >=
self.vocab_size] = 2 # <unk>
else:
inputs[item + '_unk_np'] = inputs[item + '_np']
for item in ['user', 'usdx', 'resp', 'bspn', 'aspn', 'bsdx', 'dspn']:
if not cfg.enable_aspn and item == 'aspn':
continue
if not cfg.enable_bspn and item == 'bspn':
continue
if not cfg.enable_dspn and item == 'dspn':
continue
py_list = py_batch[item]
trunc_method = 'post' if item == 'resp' else 'pre'
# max_length = cfg.max_nl_length if item in ['user', 'usdx', 'resp'] else cfg.max_span_length
inputs[item + '_np'] = utils.padSeqs(py_list,
truncated=cfg.truncated,
trunc_method=trunc_method)
if item in ['user', 'usdx', 'resp', 'bspn']:
inputs[item + '_unk_np'] = deepcopy(inputs[item + '_np'])
inputs[item + '_unk_np'][
inputs[item + '_unk_np'] >= self.vocab_size] = 2 # <unk>
else:
inputs[item + '_unk_np'] = inputs[item + '_np']
if cfg.multi_acts_training and cfg.mode == 'train':
inputs['aspn_bidx'], multi_aspn = [], []
for bidx, aspn_type_list in enumerate(py_batch['aspn_aug']):
if aspn_type_list:
for aspn_list in aspn_type_list:
random.shuffle(aspn_list)
aspn = aspn_list[
0] #choose one random act span in each act type
multi_aspn.append(aspn)
inputs['aspn_bidx'].append(bidx)
if cfg.multi_act_sampling_num > 1:
for i in range(cfg.multi_act_sampling_num):
if len(aspn_list) >= i + 2:
aspn = aspn_list[
i +
1] #choose one random act span in each act type
multi_aspn.append(aspn)
inputs['aspn_bidx'].append(bidx)
if multi_aspn:
inputs['aspn_aug_np'] = utils.padSeqs(multi_aspn,
truncated=cfg.truncated,
trunc_method='pre')
inputs['aspn_aug_unk_np'] = inputs[
'aspn_aug_np'] # [all available aspn num in the batch, T]
inputs['db_np'] = np.array(py_batch['input_pointer'])
inputs['turn_domain'] = py_batch['turn_domain']
return inputs
def wrap_result(self, result_dict, eos_syntax=None):
decode_fn = self.vocab.sentence_decode
results = []
eos_syntax = ontology.eos_tokens if not eos_syntax else eos_syntax
if cfg.bspn_mode == 'bspn':
field = [
'dial_id', 'turn_num', 'user', 'bspn_gen', 'bspn', 'resp_gen',
'resp', 'aspn_gen', 'aspn', 'dspn_gen', 'dspn', 'pointer'
]
elif not cfg.enable_dst:
field = [
'dial_id', 'turn_num', 'user', 'bsdx_gen', 'bsdx', 'resp_gen',
'resp', 'aspn_gen', 'aspn', 'dspn_gen', 'dspn', 'bspn',
'pointer'
]
else:
field = [
'dial_id', 'turn_num', 'user', 'bsdx_gen', 'bsdx', 'resp_gen',
'resp', 'aspn_gen', 'aspn', 'dspn_gen', 'dspn', 'bspn_gen',
'bspn', 'pointer'
]
if self.multi_acts_record is not None:
field.insert(7, 'multi_act_gen')
for dial_id, turns in result_dict.items():
entry = {'dial_id': dial_id, 'turn_num': len(turns)}
for prop in field[2:]:
entry[prop] = ''
results.append(entry)
for turn_no, turn in enumerate(turns):
entry = {'dial_id': dial_id}
for key in field:
if key in ['dial_id']:
continue
v = turn.get(key, '')
if key == 'turn_domain':
v = ' '.join(v)
entry[key] = decode_fn(
v, eos=eos_syntax[key]
) if key in eos_syntax and v != '' else v
results.append(entry)
return results, field
def restore(self, resp, domain, constraint_dict):
restored = resp
restored = restored.capitalize()
restored = restored.replace(' -s', 's')
restored = restored.replace(' -ly', 'ly')
restored = restored.replace(' -er', 'er')
mat_ents = self.db.get_match_num(constraint_dict, True)
ref = random.choice(self.delex_refs)
restored = restored.replace('[value_reference]', ref.upper())
restored = restored.replace('[value_car]', 'BMW')
# restored.replace('[value_phone]', '830-430-6666')
for d in domain:
constraint = constraint_dict.get(d, None)
if constraint:
if 'stay' in constraint:
restored = restored.replace('[value_stay]',
constraint['stay'])
if 'day' in constraint:
restored = restored.replace('[value_day]',
constraint['day'])
if 'people' in constraint:
restored = restored.replace('[value_people]',
constraint['people'])
if 'time' in constraint:
restored = restored.replace('[value_time]',
constraint['time'])
if 'type' in constraint:
restored = restored.replace('[value_type]',
constraint['type'])
if d in mat_ents and len(mat_ents[d]) == 0:
for s in constraint:
if s == 'pricerange' and d in [
'hotel', 'restaurant'
] and 'price]' in restored:
restored = restored.replace(
'[value_price]', constraint['pricerange'])
if s + ']' in restored:
restored = restored.replace(
'[value_%s]' % s, constraint[s])
if '[value_choice' in restored and mat_ents.get(d):
restored = restored.replace('[value_choice]',
str(len(mat_ents[d])))
if '[value_choice' in restored:
restored = restored.replace('[value_choice]',
str(random.choice([1, 2, 3, 4, 5])))
stopwords = [
"i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you",
"your", "yours", "yourself", "yourselves", "he", "him", "his",
"himself", "she", "her", "hers", "herself", "it", "its", "itself",
"they", "them", "their", "theirs", "themselves", "what", "which",
"who", "whom", "this", "that", "these", "those", "am", "is", "are",
"was", "were", "be", "been", "being", "have", "has", "had",
"having", "do", "does", "did", "doing", "a", "an", "the", "and",
"but", "if", "or", "because", "as", "until", "while", "of", "at",
"by", "for", "with", "about", "against", "between", "into",
"through", "during", "before", "after", "above", "below", "to",
"from", "up", "down", "in", "out", "on", "off", "over", "under",
"again", "further", "then", "once", "here", "there", "when",
"where", "why", "how", "all", "any", "both", "each", "few", "more",
"most", "other", "some", "such", "no", "nor", "not", "only", "own",
"same", "so", "than", "too", "very", "s", "t", "can", "will",
"just", "don", "should", "now"
]
# restored.replace('[value_car]', 'BMW')
ent = mat_ents.get(domain[-1], [])
if ent:
# handle multiple [value_xxx] tokens first
restored_split = restored.split()
token_count = Counter(restored_split)
for idx, t in enumerate(restored_split):
if '[value' in t and token_count[t] > 1 and token_count[
t] <= len(ent):
slot = t[7:-1]
pattern = r'\[' + t[1:-1] + r'\]'
for e in ent:
if e.get(slot):
if domain[-1] == 'hotel' and slot == 'price':
slot = 'pricerange'
if slot in ['name', 'address']:
rep = ' '.join([
i.capitalize() if i not in stopwords else i
for i in e[slot].split()
])
elif slot in ['id', 'postcode']:
rep = e[slot].upper()
else:
rep = e[slot]
restored = re.sub(pattern, rep, restored, 1)
elif slot == 'price' and e.get('pricerange'):
restored = re.sub(pattern, e['pricerange'],
restored, 1)
# handle normal 1 entity case
ent = ent[0]
for t in restored.split():
if '[value' in t:
slot = t[7:-1]
if ent.get(slot):
if domain[-1] == 'hotel' and slot == 'price':
slot = 'pricerange'
if slot in ['name', 'address']:
rep = ' '.join([
i.capitalize() if i not in stopwords else i
for i in ent[slot].split()
])
elif slot in ['id', 'postcode']:
rep = ent[slot].upper()
else:
rep = ent[slot]
# rep = ent[slot]
restored = restored.replace(t, rep)
# restored = restored.replace(t, ent[slot])
elif slot == 'price' and ent.get('pricerange'):
restored = restored.replace(t, ent['pricerange'])
# else:
# print(restored, domain)
for t in restored.split():
if '[value' in t:
restored = restored.replace(t, 'UNKNOWN')
restored = restored.split()
for idx, w in enumerate(restored):
if idx > 0 and restored[idx - 1] in ['.', '?', '!']:
restored[idx] = restored[idx].capitalize()
restored = ' '.join(restored)
return restored
# def restore(self, resp, domain, constraint_dict, mat_ents):
# restored = resp
# restored = restored.replace('[value_reference]', '53022')
# restored = restored.replace('[value_car]', 'BMW')
# # restored.replace('[value_phone]', '830-430-6666')
# for d in domain:
# constraint = constraint_dict.get(d,None)
# if constraint:
# if 'stay' in constraint:
# restored = restored.replace('[value_stay]', constraint['stay'])
# if 'day' in constraint:
# restored = restored.replace('[value_day]', constraint['day'])
# if 'people' in constraint:
# restored = restored.replace('[value_people]', constraint['people'])
# if 'time' in constraint:
# restored = restored.replace('[value_time]', constraint['time'])
# if 'type' in constraint:
# restored = restored.replace('[value_type]', constraint['type'])
# if d in mat_ents and len(mat_ents[d])==0:
# for s in constraint:
# if s == 'pricerange' and d in ['hotel', 'restaurant'] and 'price]' in restored:
# restored = restored.replace('[value_price]', constraint['pricerange'])
# if s+']' in restored:
# restored = restored.replace('[value_%s]'%s, constraint[s])
# if '[value_choice' in restored and mat_ents.get(d):
# restored = restored.replace('[value_choice]', str(len(mat_ents[d])))
# if '[value_choice' in restored:
# restored = restored.replace('[value_choice]', '3')
# # restored.replace('[value_car]', 'BMW')
# try:
# ent = mat_ents.get(domain[-1], [])
# if ent:
# ent = ent[0]
# for t in restored.split():
# if '[value' in t:
# slot = t[7:-1]
# if ent.get(slot):
# if domain[-1] == 'hotel' and slot == 'price':
# slot = 'pricerange'
# restored = restored.replace(t, ent[slot])
# elif slot == 'price':
# if ent.get('pricerange'):
# restored = restored.replace(t, ent['pricerange'])
# else:
# print(restored, domain)
# except:
# print(resp)
# print(restored)
# quit()
# restored = restored.replace('[value_phone]', '62781111')
# restored = restored.replace('[value_postcode]', 'CG9566')
# restored = restored.replace('[value_address]', 'Parkside, Cambridge')
# return restored
def record_utterance(self, result_dict):
decode_fn = self.vocab.sentence_decode
ordered_dial = {}
for dial_id, turns in result_dict.items():
diverse = 0
turn_count = 0
for turn_no, turn in enumerate(turns):
act_collect = {}
act_type_collect = {}
slot_score = 0
for i in range(cfg.nbest):
aspn = decode_fn(turn['multi_act'][i],
eos=ontology.eos_tokens['aspn'])
pred_acts = self.aspan_to_act_list(' '.join(aspn))
act_type = ''
for act in pred_acts:
d, a, s = act.split('-')
if d + '-' + a not in act_collect:
act_collect[d + '-' + a] = {s: 1}
slot_score += 1
act_type += d + '-' + a + ';'
elif s not in act_collect:
act_collect[d + '-' + a][s] = 1
slot_score += 1
act_type_collect[act_type] = 1
turn_count += 1
diverse += len(act_collect) * 3 + slot_score
ordered_dial[dial_id] = diverse / turn_count
ordered_dial = sorted(ordered_dial.keys(),
key=lambda x: -ordered_dial[x])
dialog_record = {}
with open(cfg.eval_load_path + '/dialogue_record.csv', 'w') as rf:
writer = csv.writer(rf)
for dial_id in ordered_dial:
dialog_record[dial_id] = []
turns = result_dict[dial_id]
writer.writerow([dial_id])
for turn_no, turn in enumerate(turns):
user = decode_fn(turn['user'],
eos=ontology.eos_tokens['user'])
bspn = decode_fn(turn['bspn'],
eos=ontology.eos_tokens['bspn'])
aspn = decode_fn(turn['aspn'],
eos=ontology.eos_tokens['aspn'])
resp = decode_fn(turn['resp'],
eos=ontology.eos_tokens['resp'])
constraint_dict = self.bspan_to_constraint_dict(bspn)
# print(constraint_dict)
mat_ents = self.db.get_match_num(constraint_dict, True)
domain = [
i[1:-1]
for i in self.dspan_to_domain(turn['dspn']).keys()
]
restored = self.restore(resp, domain, constraint_dict,
mat_ents)
writer.writerow([
turn_no, user, turn['pointer'], domain, restored, resp
])
turn_record = {
'user': user,
'bspn': bspn,
'aspn': aspn,
'dom': domain,
'resp': resp,
'resp_res': restored
}
resp_col = []
aspn_col = []
resp_restore_col = []
for i in range(cfg.nbest):
aspn = decode_fn(turn['multi_act'][i],
eos=ontology.eos_tokens['aspn'])
resp = decode_fn(turn['multi_resp'][i],
eos=ontology.eos_tokens['resp'])
restored = self.restore(resp, domain, constraint_dict,
mat_ents)
resp_col.append(resp)
resp_restore_col.append(restored)
aspn_col.append(aspn)
zipped = list(zip(resp_restore_col, resp_col, aspn_col))
zipped.sort(key=lambda s: len(s[0]))
resp_restore_col = list(list(zip(*zipped))[0])
aspn_col = list(list(zip(*zipped))[2])
resp_col = list(list(zip(*zipped))[1])
turn_record['aspn_col'] = aspn_col
turn_record['resp_col'] = resp_col
turn_record['resp_res_col'] = resp_restore_col
for i in range(cfg.nbest):
# aspn = decode_fn(turn['multi_act'][i], eos=ontology.eos_tokens['aspn'])
resp = resp_col[i]
aspn = aspn_col[i]
resp_restore = resp_restore_col[i]
writer.writerow(['', resp_restore, resp, aspn])
dialog_record[dial_id].append(turn_record)