def _build_full_act(self, user_act_queue, belief_space, a):
grammar = Grammar('decision')
sys_da = DialogueAct()
if a == 'accept' or a == 'delay' or a == 'reject':
dai = DialogueActItem('end')
sys_da = DialogueAct(dai)
dai = DialogueActItem(
a, {
'appointment': {
'person': 'Mr. Bean',
'time': 'next Monday at 9.00 AM'
}
})
sys_da.append(dai)
else:
dai = DialogueActItem(
'request', {
'decision': ['accept', 'delay', 'reject'],
'appointment': {
'person': 'Mr. Bean',
'time': 'next Monday at 9.00 AM'
}
}, grammar)
sys_da.append(dai)
if self.last_sys_act == None:
dai = DialogueActItem('hello', {})
sys_da.append(dai)
elif a == 'request':
dai = DialogueActItem('sorry', {})
sys_da.append(dai)
return sys_da
def build_full_act(self, user_act_queue, belief_space, a):
'''Buiding action and full detailed information to transfer to NLG e.g. request(price=[cheap, moderate, expersive])'''
#TODO: need to track dialogue history to build suitable action
#appointment = {'person': 'Mr. Bean', 'time': 'next Monday at 9.00 AM', 'reason': 'the new OS'}
grammar = Grammar('decision')
sys_da = DialogueAct()
if a=='accept' or a=='delay' or a=='reject':
dai = DialogueActItem('end')
sys_da = DialogueAct(dai)
dai = DialogueActItem(a, {'appointment': self.appointment})
sys_da.append(dai)
else:
dai = DialogueActItem('request', {'decision':['accept','delay', 'reject'], 'appointment': self.appointment}, grammar)
sys_da.append(dai)
if self.last_sys_act==None:
dai = DialogueActItem('hello', {})
sys_da.append(dai)
elif a=='request':
dai = DialogueActItem('sorry', {})
sys_da.append(dai)
self.last_sys_act = sys_da
return sys_da
def get_da(self, user_act_queue, belief_tracker):
user_act = None #Currently not use user_act
if len(user_act_queue) != 0:
user_act = user_act_queue.pop()
grammar = Grammar('decision')
sys_da = DialogueAct()
belief_state = belief_tracker.get_belief_space()
if belief_state is not None:
max_assign, prob = belief_state.get_nbest_assignments(1)[0]
if prob >= self._do_threshold: #do action and end dialog
dai = DialogueActItem('end')
sys_da = DialogueAct(dai)
dai = DialogueActItem(
str(max_assign), {
'appointment': {
'person': 'Mr. Bean',
'time': 'next Monday at 9.00 AM'
}
})
sys_da.append(dai)
return sys_da
dai = DialogueActItem(
'request', {
'decision': ['accept', 'delay', 'reject'],
'appointment': {
'person': 'Mr. Bean',
'time': 'next Monday at 9.00 AM'
}
}, grammar)
sys_da = DialogueAct(dai)
if self.last_sys_act == None:
dai = DialogueActItem('hello', {})
sys_da.append(dai)
else:
dai = DialogueActItem('sorry', {})
sys_da.append(dai)
self.last_sys_act = sys_da
return self.last_sys_act
with open(config.grammar_file, 'ra') as grammar_file:
# Select the parser and rule class to use
if config.parser == 'td':
parser_class = ParserTD
rule_class = TdRule
if config.boundary_nodes:
parser_class.item_class = Item
elif config.parser == 'basic':
parser_class = Parser
rule_class = VoRule
# Read the grammar
grammar = Grammar.load_from_file(grammar_file,
rule_class,
config.backward,
nodelabels=(not config.edge_labels),
logprob=logprob)
if config.start_symbol:
grammar.start_symbol = config.start_symbol
if len(grammar) == 0:
log.err("Unable to load grammar from file.")
sys.exit(1)
log.info("Loaded %s%s grammar with %i rules."\
% (grammar.rhs1_type, "-to-%s" % grammar.rhs2_type if grammar.rhs2_type else '', len(grammar)))
# EM training
if config.train:
iterations = config.train
if not config.input_file:
output_file = sys.stdout
with open(config.grammar_file,'r') as grammar_file:
# Select the parser and rule class to use
if config.parser == 'td':
parser_class = ParserTD
rule_class = TdRule
if config.boundary_nodes:
parser_class.item_class = Item
elif config.parser == 'basic':
parser_class = Parser
rule_class = VoRule
# Read the grammar
grammar = Grammar.load_from_file(grammar_file, rule_class, config.backward, nodelabels = (not config.edge_labels), logprob = logprob)
if config.start_symbol:
grammar.start_symbol = config.start_symbol
if len(grammar) == 0:
log.err("Unable to load grammar from file.")
sys.exit(1)
log.info("Loaded %s%s grammar with %i rules."\
% (grammar.rhs1_type, "-to-%s" % grammar.rhs2_type if grammar.rhs2_type else '', len(grammar)))
# EM training
if config.train:
iterations = config.train
if not config.input_file:
log.err("Please specify corpus file for EM training.")
stack.append(subtree)
state = 1
else: raise ParserError, "Unexpected token %s at position %i." % (token, pos)
elif state == 3:
if typ == DerivationLexTypes.LPAR:
state = 0
elif typ == DerivationLexTypes.RPAR:
children = []
while stack and type(stack[-1]) is tuple:
children.append(stack.pop())
parent = stack.pop()
subtree = ((parent,dict(children)))
stack.append(subtree)
state = 2
else: raise ParserError, "Unexpected token %s at position %i." % (token, pos)
assert len(stack)==1
return stack[0]
if __name__ == "__main__":
grammar = Grammar.load_from_file(open(sys.argv[1],'r'), VoRule)
parser = DerivationParser(grammar)
for line in fileinput.input("-"):
line = line.split("#")[0].strip()
derivation = parser.parse_string(line.strip())
print " ".join(output.apply_string_derivation(derivation))