def generate_training_instances(parsing_system: ParsingSystem,
sentences: List[List[str]],
vocabulary: Vocabulary,
trees: List[DependencyTree]) -> List[Dict]:
"""
Generates training instances of configuration and transition labels
from the sentences and the corresponding dependency trees.
"""
num_transitions = parsing_system.num_transitions()
instances: Dict[str, List] = []
for i in tqdm(range(len(sentences))):
if trees[i].is_projective():
c = parsing_system.initial_configuration(sentences[i])
while not parsing_system.is_terminal(c):
oracle = parsing_system.get_oracle(c, trees[i])
feature = get_configuration_features(c, vocabulary)
label = []
for j in range(num_transitions):
t = parsing_system.transitions[j]
if t == oracle:
label.append(1.)
elif parsing_system.can_apply(c, t):
label.append(0.)
else:
label.append(-1.)
if 1.0 not in label:
print(i, label)
instances.append({"input": feature, "label": label})
c = parsing_system.apply(c, oracle)
return instances
def predict(model: models.Model, sentences: List[Sentence],
parsing_system: ParsingSystem,
vocabulary: Vocabulary) -> List[DependencyTree]:
"""
Predicts the dependency tree for a given sentence by greedy decoding.
We generate a initial configuration (features) for ``sentence`` using
``parsing_system`` and ``vocabulary``. Then we apply the ``model`` to predict
what's the best transition for this configuration and apply this transition
(greedily) with ``parsing_system`` to get the next configuration. We do
this till the terminal configuration is reached.
"""
predicted_trees = []
num_transitions = parsing_system.num_transitions()
for sentence in tqdm(sentences):
configuration = parsing_system.initial_configuration(sentence)
while not parsing_system.is_terminal(configuration):
features = get_configuration_features(configuration, vocabulary)
features = np.array(features).reshape((1, -1))
logits = model(features)["logits"].numpy()
opt_score = -float('inf')
opt_trans = ""
for j in range(num_transitions):
if (logits[0, j] > opt_score and parsing_system.can_apply(
configuration, parsing_system.transitions[j])):
opt_score = logits[0, j]
opt_trans = parsing_system.transitions[j]
configuration = parsing_system.apply(configuration, opt_trans)
predicted_trees.append(configuration.tree)
return predicted_trees