def __call__(self, query, options, gold, lengths, query_no):
if len(options) == 1:
return None, 0
final = []
if args.word_vectors:
qvecs = [dy.lookup(self.pEmbedding, w) for w in query]
qvec_max = dy.emax(qvecs)
qvec_mean = dy.average(qvecs)
for otext, features in options:
if not args.no_features:
inputs = dy.inputTensor(features)
if args.word_vectors:
ovecs = [dy.lookup(self.pEmbedding, w) for w in otext]
ovec_max = dy.emax(ovecs)
ovec_mean = dy.average(ovecs)
if args.no_features:
inputs = dy.concatenate(
[qvec_max, qvec_mean, ovec_max, ovec_mean])
else:
inputs = dy.concatenate(
[inputs, qvec_max, qvec_mean, ovec_max, ovec_mean])
if args.drop > 0:
inputs = dy.dropout(inputs, args.drop)
h = inputs
for pH, pB in zip(self.hidden, self.bias):
h = dy.affine_transform([pB, pH, h])
if args.nonlin == "linear":
pass
elif args.nonlin == "tanh":
h = dy.tanh(h)
elif args.nonlin == "cube":
h = dy.cube(h)
elif args.nonlin == "logistic":
h = dy.logistic(h)
elif args.nonlin == "relu":
h = dy.rectify(h)
elif args.nonlin == "elu":
h = dy.elu(h)
elif args.nonlin == "selu":
h = dy.selu(h)
elif args.nonlin == "softsign":
h = dy.softsign(h)
elif args.nonlin == "swish":
h = dy.cmult(h, dy.logistic(h))
final.append(dy.sum_dim(h, [0]))
final = dy.concatenate(final)
nll = -dy.log_softmax(final)
dense_gold = []
for i in range(len(options)):
dense_gold.append(1.0 / len(gold) if i in gold else 0.0)
answer = dy.inputTensor(dense_gold)
loss = dy.transpose(answer) * nll
predicted_link = np.argmax(final.npvalue())
return loss, predicted_link
def parse(self, t, oracle_actions=None):
dy.renew_cg()
self.NULL_REP = self.WORDS_LOOKUP[self.nwords - 1]
if oracle_actions:
oracle_actions = list(oracle_actions)
oracle_actions.reverse()
toks = list(t)
toks.reverse()
stack = []
buffer = []
W1 = dy.parameter(self.pW1)
b1 = dy.parameter(self.pb1)
W_act = dy.parameter(self.pW_act)
b_act = dy.parameter(self.pb_act)
losses = []
for tok in toks:
tok_embedding = self.WORDS_LOOKUP[tok]
buffer.append(Head(self.vocab.i2w[tok], tok_embedding))
while not (len(stack) == 1 and len(buffer) == 0):
# based on parser state, get valid actions
valid_actions = []
if len(buffer) > 0: # can only reduce if elements in buffer
valid_actions += [SHIFT]
if len(stack) >= 2: # can only shift if 2 elements on stack
valid_actions += [REDUCE_L, REDUCE_R]
# compute probability of each of the actions and choose an action
# either from the oracle or if there is no oracle, based on the model
action = valid_actions[0]
log_probs = None
if len(valid_actions) > 1:
representations = self.extract_features(stack, buffer)
h = dy.cube(W1 * dy.concatenate(representations) + b1)
logits = W_act * h + b_act
log_probs = dy.log_softmax(logits, valid_actions)
if oracle_actions is None:
action = max(enumerate(log_probs.vec_value()),
key=itemgetter(1))[0]
if oracle_actions is not None:
action = oracle_actions.pop()
if log_probs is not None:
# append the action-specific loss
losses.append(dy.pick(log_probs, action))
# execute the action to update the parser state
if action == SHIFT:
token = buffer.pop()
stack.append(token)
else: # one of the reduce actions
right = stack.pop()
left = stack.pop()
head, modifier = (left,
right) if action == REDUCE_R else (right,
left)
#add the tokens and their embeddings into the children list
if action == REDUCE_R:
head.add_child(modifier, 'right')
else:
head.add_child(modifier, 'left')
stack.append(head)
if oracle_actions is None:
print('{0} --> {1}'.format(head.word, modifier.word))
# the head of the tree that remains at the top of the stack is now the root
if oracle_actions is None:
head = stack.pop().word
print('ROOT --> {0}'.format(head))
return -dy.esum(losses) if losses else None
def parse(self, t, oracle_actions=None):
dy.renew_cg()
self.NULL_REP = self.WORDS_LOOKUP[self.nwords-1]
if oracle_actions:
oracle_actions = list(oracle_actions)
oracle_actions.reverse()
toks = list(t)
toks.reverse()
stack = []
buffer = []
W1 = dy.parameter(self.pW1)
b1 = dy.parameter(self.pb1)
W_act = dy.parameter(self.pW_act)
b_act = dy.parameter(self.pb_act)
losses = []
for tok in toks:
tok_embedding = self.WORDS_LOOKUP[tok]
buffer.append(Head(self.vocab.i2w[tok], tok_embedding))
while not (len(stack) == 1 and len(buffer) == 0):
# based on parser state, get valid actions
valid_actions = []
if len(buffer) > 0: # can only reduce if elements in buffer
valid_actions += [SHIFT]
if len(stack) >= 2: # can only shift if 2 elements on stack
valid_actions += [REDUCE_L, REDUCE_R]
# compute probability of each of the actions and choose an action
# either from the oracle or if there is no oracle, based on the model
action = valid_actions[0]
log_probs = None
if len(valid_actions) > 1:
representations = self.extract_features(stack, buffer)
h = dy.cube(W1*dy.concatenate(representations) + b1)
logits = W_act * h + b_act
log_probs = dy.log_softmax(logits, valid_actions)
if oracle_actions is None:
action = max(enumerate(log_probs.vec_value()), key=itemgetter(1))[0]
if oracle_actions is not None:
action = oracle_actions.pop()
if log_probs is not None:
# append the action-specific loss
losses.append(dy.pick(log_probs, action))
# execute the action to update the parser state
if action == SHIFT:
token = buffer.pop()
stack.append(token)
else: # one of the reduce actions
right = stack.pop()
left = stack.pop()
head, modifier = (left, right) if action == REDUCE_R else (right, left)
#add the tokens and their embeddings into the children list
if action == REDUCE_R:
head.add_child(modifier, 'right')
else:
head.add_child(modifier, 'left')
stack.append(head)
if oracle_actions is None:
print('{0} --> {1}'.format(head.word, modifier.word))
# the head of the tree that remains at the top of the stack is now the root
if oracle_actions is None:
head = stack.pop().word
print('ROOT --> {0}'.format(head))
return -dy.esum(losses) if losses else None
def cube(x):
return dy.cube(x)