# allennlp.models.coreference_resolution¶

class allennlp.models.coreference_resolution.coref.CoreferenceResolver(vocab: allennlp.data.vocabulary.Vocabulary, text_field_embedder: allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder, context_layer: allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder, mention_feedforward: allennlp.modules.feedforward.FeedForward, antecedent_feedforward: allennlp.modules.feedforward.FeedForward, feature_size: int, max_span_width: int, spans_per_word: float, max_antecedents: int, lexical_dropout: float = 0.2, initializer: allennlp.nn.initializers.InitializerApplicator = <allennlp.nn.initializers.InitializerApplicator object>, regularizer: Optional[allennlp.nn.regularizers.regularizer_applicator.RegularizerApplicator] = None)[source]

This Model implements the coreference resolution model described “End-to-end Neural Coreference Resolution” <https://www.semanticscholar.org/paper/End-to-end-Neural-Coreference-Resolution-Lee-He/3f2114893dc44eacac951f148fbff142ca200e83> by Lee et al., 2017. The basic outline of this model is to get an embedded representation of each span in the document. These span representations are scored and used to prune away spans that are unlikely to occur in a coreference cluster. For the remaining spans, the model decides which antecedent span (if any) they are coreferent with. The resulting coreference links, after applying transitivity, imply a clustering of the spans in the document.

Parameters
vocabVocabulary
text_field_embedderTextFieldEmbedder

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

context_layerSeq2SeqEncoder

This layer incorporates contextual information for each word in the document.

mention_feedforwardFeedForward

This feedforward network is applied to the span representations which is then scored by a linear layer.

antecedent_feedforward: FeedForward

This feedforward network is applied to pairs of span representation, along with any pairwise features, which is then scored by a linear layer.

feature_size: int

The embedding size for all the embedded features, such as distances or span widths.

max_span_width: int

The maximum width of candidate spans.

spans_per_word: float, required.

A multiplier between zero and one which controls what percentage of candidate mention spans we retain with respect to the number of words in the document.

max_antecedents: int, required.

For each mention which survives the pruning stage, we consider this many antecedents.

lexical_dropout: int

The probability of dropping out dimensions of the embedded text.

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])[source]

Converts the list of spans and predicted antecedent indices into clusters of spans for each element in the batch.

Parameters
output_dictDict[str, torch.Tensor], required.

The result of calling forward() on an instance or batch of instances.

Returns
The same output dictionary, but with an additional clusters key:
clustersList[List[List[Tuple[int, int]]]]

A nested list, representing, for each instance in the batch, the list of clusters, which are in turn comprised of a list of (start, end) inclusive spans into the original document.

forward(self, text:Dict[str, torch.LongTensor], spans:torch.IntTensor, span_labels:torch.IntTensor=None, metadata:List[Dict[str, Any]]=None) → Dict[str, torch.Tensor][source]
Parameters
textDict[str, torch.LongTensor], required.

The output of a TextField representing the text of the document.

spanstorch.IntTensor, required.

A tensor of shape (batch_size, num_spans, 2), representing the inclusive start and end indices of candidate spans for mentions. Comes from a ListField[SpanField] of indices into the text of the document.

span_labelstorch.IntTensor, optional (default = None).

A tensor of shape (batch_size, num_spans), representing the cluster ids of each span, or -1 for those which do not appear in any clusters.

metadataList[Dict[str, Any]], optional (default = None).

A metadata dictionary for each instance in the batch. We use the “original_text” and “clusters” keys from this dictionary, which respectively have the original text and the annotated gold coreference clusters for that instance.

Returns
An output dictionary consisting of:
top_spanstorch.IntTensor

A tensor of shape (batch_size, num_spans_to_keep, 2) representing the start and end word indices of the top spans that survived the pruning stage.

antecedent_indicestorch.IntTensor

A tensor of shape (num_spans_to_keep, max_antecedents) representing for each top span the index (with respect to top_spans) of the possible antecedents the model considered.

predicted_antecedentstorch.IntTensor

A tensor of shape (batch_size, num_spans_to_keep) representing, for each top span, the index (with respect to antecedent_indices) of the most likely antecedent. -1 means there was no predicted link.

losstorch.FloatTensor, optional

A scalar loss to be optimised.

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

Returns a dictionary of metrics. This method will be called by allennlp.training.Trainer 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.