def process_dialog(cls, msg, use_task=True):
"""
Dialog strategy: use sub-task to handle dialog firstly,
if failed, use retrieval or generational func to handle it.
"""
# Task response.
if use_task:
task_response, cls.dialog_status = TaskCore.task_handle(
msg, cls.dialog_status)
else:
task_response = None
# Search response.
if len(cls.dialog_status.context) >= 3 and ch_count(msg) <= 4:
user_msgs = cls.dialog_status.context[::2][-3:]
msg = "<s>".join(user_msgs)
mode = "cr"
else:
mode = "qa"
msg_tokens = NlpUtil.tokenize(msg, True)
search_response, sim_score = SearchCore.search(msg_tokens, mode=mode)
# Seq2seq response.
seq2seq_response = cls._predict_via_seq2seq(msg_tokens)
log_print("search_response=%s" % search_response)
log_print("seq2seq_response=%s" % seq2seq_response)
if task_response:
response = task_response
elif sim_score >= 1.0:
response = search_response
else:
response = seq2seq_response
return response
def intent_update(msg, dialog_status):
if (ch_count(msg) <= 8 and not not_match_pattern.search(msg)
and len(dialog_status.context) == 1 and start_pattern.search(msg)
and not dialog_status.start_flag):
dialog_status.intent = "start"
dialog_status.start_flag = True
return dialog_status
def intent_update(msg, dialog_status):
msg = bracket_pattern.sub("括", msg)
if ch_count(msg) <= 4 and match_pattern.search(msg):
if not ch_pattern.search(msg) or not not_match_pattern.search(msg):
dialog_status.intent = "short_query"
return dialog_status
def intent_update(msg,dialog_status):
if ch_count(msg) <= 8 and finish_pattern.search(msg):
dialog_status.intent = "finish"
return dialog_status