class allennlp.models.biaffine_dependency_parser.BiaffineDependencyParser(vocab:, text_field_embedder: allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder, encoder: allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder, tag_representation_dim: int, arc_representation_dim: int, tag_feedforward: allennlp.modules.feedforward.FeedForward = None, arc_feedforward: allennlp.modules.feedforward.FeedForward = None, pos_tag_embedding: allennlp.modules.token_embedders.embedding.Embedding = None, use_mst_decoding_for_validation: bool = True, dropout: float = 0.0, input_dropout: float = 0.0, initializer: allennlp.nn.initializers.InitializerApplicator = <allennlp.nn.initializers.InitializerApplicator object>, regularizer: Optional[allennlp.nn.regularizers.regularizer_applicator.RegularizerApplicator] = None)[source]

Bases: allennlp.models.model.Model

This dependency parser follows the model of ` Deep Biaffine Attention for Neural Dependency Parsing (Dozat and Manning, 2016) <>`_ .

Word representations are generated using a bidirectional LSTM, followed by separate biaffine classifiers for pairs of words, predicting whether a directed arc exists between the two words and the dependency label the arc should have. Decoding can either be done greedily, or the optimal Minimum Spanning Tree can be decoded using Edmond’s algorithm by viewing the dependency tree as a MST on a fully connected graph, where nodes are words and edges are scored dependency arcs.

vocabVocabulary, required

A Vocabulary, required in order to compute sizes for input/output projections.

text_field_embedderTextFieldEmbedder, required

Used to embed the tokens TextField we get as input to the model.


The encoder (with its own internal stacking) that we will use to generate representations of tokens.

tag_representation_dimint, required.

The dimension of the MLPs used for dependency tag prediction.

arc_representation_dimint, required.

The dimension of the MLPs used for head arc prediction.

tag_feedforwardFeedForward, optional, (default = None).

The feedforward network used to produce tag representations. By default, a 1 layer feedforward network with an elu activation is used.

arc_feedforwardFeedForward, optional, (default = None).

The feedforward network used to produce arc representations. By default, a 1 layer feedforward network with an elu activation is used.

pos_tag_embeddingEmbedding, optional.

Used to embed the pos_tags SequenceLabelField we get as input to the model.

use_mst_decoding_for_validationbool, optional (default = True).

Whether to use Edmond’s algorithm to find the optimal minimum spanning tree during validation. If false, decoding is greedy.

dropoutfloat, optional, (default = 0.0)

The variational dropout applied to the output of the encoder and MLP layers.

input_dropoutfloat, optional, (default = 0.0)

The dropout applied to the embedded text input.

initializerInitializerApplicator, optional (default=``InitializerApplicator()``)

Used to initialize the model parameters.

regularizerRegularizerApplicator, optional (default=``None``)

If provided, will be used to calculate the regularization penalty during training.

decode(self, output_dict: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor][source]

Takes the result of forward() and runs inference / decoding / whatever post-processing you need to do your model. The intent is that model.forward() should produce potentials or probabilities, and then model.decode() can take those results and run some kind of beam search or constrained inference or whatever is necessary. This does not handle all possible decoding use cases, but it at least handles simple kinds of decoding.

This method modifies the input dictionary, and also returns the same dictionary.

By default in the base class we do nothing. If your model has some special decoding step, override this method.

forward(self, words: Dict[str, torch.LongTensor], pos_tags: torch.LongTensor, metadata: List[Dict[str, Any]], head_tags: torch.LongTensor = None, head_indices: torch.LongTensor = None) → Dict[str, torch.Tensor][source]
wordsDict[str, torch.LongTensor], required

The output of TextField.as_array(), which should typically be passed directly to a TextFieldEmbedder. This output is a dictionary mapping keys to TokenIndexer tensors. At its most basic, using a SingleIdTokenIndexer this is: {"tokens": Tensor(batch_size, sequence_length)}. This dictionary will have the same keys as were used for the TokenIndexers when you created the TextField representing your sequence. The dictionary is designed to be passed directly to a TextFieldEmbedder, which knows how to combine different word representations into a single vector per token in your input.

pos_tagstorch.LongTensor, required

The output of a SequenceLabelField containing POS tags. POS tags are required regardless of whether they are used in the model, because they are used to filter the evaluation metric to only consider heads of words which are not punctuation.

metadataList[Dict[str, Any]], optional (default=None)
A dictionary of metadata for each batch element which has keys:
wordsList[str], required.

The tokens in the original sentence.

posList[str], required.

The dependencies POS tags for each word.

head_tagstorch.LongTensor, optional (default = None)

A torch tensor representing the sequence of integer gold class labels for the arcs in the dependency parse. Has shape (batch_size, sequence_length).

head_indicestorch.LongTensor, optional (default = None)

A torch tensor representing the sequence of integer indices denoting the parent of every word in the dependency parse. Has shape (batch_size, sequence_length).

An output dictionary consisting of:
losstorch.FloatTensor, optional

A scalar loss to be optimised.


The loss contribution from the unlabeled arcs.

losstorch.FloatTensor, optional

The loss contribution from predicting the dependency tags for the gold arcs.


The predicted head indices for each word. A tensor of shape (batch_size, sequence_length).


The predicted head types for each arc. A tensor of shape (batch_size, sequence_length).


A mask denoting the padded elements in the batch.

get_metrics(self, reset: bool = False) → Dict[str, float][source]

Returns a dictionary of metrics. This method will be called by in order to compute and use model metrics for early stopping and model serialization. We return an empty dictionary here rather than raising as it is not required to implement metrics for a new model. A boolean reset parameter is passed, as frequently a metric accumulator will have some state which should be reset between epochs. This is also compatible with Metrics should be populated during the call to ``forward`, with the Metric handling the accumulation of the metric until this method is called.