# allennlp.models.semantic_role_labeler¶

class allennlp.models.semantic_role_labeler.SemanticRoleLabeler(vocab: allennlp.data.vocabulary.Vocabulary, text_field_embedder: allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder, encoder: allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder, binary_feature_dim: int, embedding_dropout: float = 0.0, initializer: allennlp.nn.initializers.InitializerApplicator = <allennlp.nn.initializers.InitializerApplicator object>, regularizer: Optional[allennlp.nn.regularizers.regularizer_applicator.RegularizerApplicator] = None, label_smoothing: float = None, ignore_span_metric: bool = False, srl_eval_path: str = '/local/deploy/agent6/work/8feb324ce7c68d53/allennlp/tools/srl-eval.pl')[source]

This model performs semantic role labeling using BIO tags using Propbank semantic roles. Specifically, it is an implementation of Deep Semantic Role Labeling - What works and what’s next .

This implementation is effectively a series of stacked interleaved LSTMs with highway connections, applied to embedded sequences of words concatenated with a binary indicator containing whether or not a word is the verbal predicate to generate predictions for in the sentence. Additionally, during inference, Viterbi decoding is applied to constrain the predictions to contain valid BIO sequences.

Specifically, the model expects and outputs IOB2-formatted tags, where the B- tag is used in the beginning of every chunk (i.e. all chunks start with the B- tag).

Parameters
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.

encoderSeq2SeqEncoder

The encoder (with its own internal stacking) that we will use in between embedding tokens and predicting output tags.

binary_feature_dimint, required.

The dimensionality of the embedding of the binary verb predicate features.

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.

label_smoothingfloat, optional (default = 0.0)

Whether or not to use label smoothing on the labels when computing cross entropy loss.

ignore_span_metric: bool, optional (default = False)

Whether to calculate span loss, which is irrelevant when predicting BIO for Open Information Extraction.

srl_eval_path: str, optional (default=DEFAULT_SRL_EVAL_PATH)

The path to the srl-eval.pl script. By default, will use the srl-eval.pl included with allennlp, which is located at allennlp/tools/srl-eval.pl . If None, srl-eval.pl is not used.

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

Does constrained viterbi decoding on class probabilities output in forward(). The constraint simply specifies that the output tags must be a valid BIO sequence. We add a "tags" key to the dictionary with the result.

forward(self, tokens:Dict[str, torch.LongTensor], verb_indicator:torch.LongTensor, tags:torch.LongTensor=None, metadata:List[Dict[str, Any]]=None) → Dict[str, torch.Tensor][source]
Parameters
tokensDict[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, num_tokens)}. 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.

verb_indicator: torch.LongTensor, required.

An integer SequenceFeatureField representation of the position of the verb in the sentence. This should have shape (batch_size, num_tokens) and importantly, can be all zeros, in the case that the sentence has no verbal predicate.

tagstorch.LongTensor, optional (default = None)

A torch tensor representing the sequence of integer gold class labels of shape (batch_size, num_tokens)

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

metadata containg the original words in the sentence and the verb to compute the frame for, under ‘words’ and ‘verb’ keys, respectively.

Returns
An output dictionary consisting of:
logitstorch.FloatTensor

A tensor of shape (batch_size, num_tokens, tag_vocab_size) representing unnormalised log probabilities of the tag classes.

class_probabilitiestorch.FloatTensor

A tensor of shape (batch_size, num_tokens, tag_vocab_size) representing a distribution of the tag classes per word.

losstorch.FloatTensor, optional

A scalar loss to be optimised.

get_metrics(self, reset:bool=False)[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.

get_start_transitions(self)[source]

In the BIO sequence, we cannot start the sequence with an I-XXX tag. This transition sequence is passed to viterbi_decode to specify this constraint.

Returns
start_transitionstorch.Tensor

The pairwise potentials between a START token and the first token of the sequence.

get_viterbi_pairwise_potentials(self)[source]

Generate a matrix of pairwise transition potentials for the BIO labels. The only constraint implemented here is that I-XXX labels must be preceded by either an identical I-XXX tag or a B-XXX tag. In order to achieve this constraint, pairs of labels which do not satisfy this constraint have a pairwise potential of -inf.

Returns
transition_matrixtorch.Tensor

A (num_labels, num_labels) matrix of pairwise potentials.

allennlp.models.semantic_role_labeler.write_to_conll_eval_file(prediction_file:TextIO, gold_file:TextIO, verb_index:Union[int, NoneType], sentence:List[str], prediction:List[str], gold_labels:List[str])[source]

Deprecated since version 0.8.4: The write_to_conll_eval_file function was deprecated in favor of the identical write_bio_formatted_tags_to_file` in version 0.8.4.

Prints predicate argument predictions and gold labels for a single verbal predicate in a sentence to two provided file references.

The CoNLL SRL format is described in the shared task data README .

This function expects IOB2-formatted tags, where the B- tag is used in the beginning of every chunk (i.e. all chunks start with the B- tag).

Parameters
prediction_fileTextIO, required.

A file reference to print predictions to.

gold_fileTextIO, required.

A file reference to print gold labels to.

verb_indexOptional[int], required.

The index of the verbal predicate in the sentence which the gold labels are the arguments for, or None if the sentence contains no verbal predicate.

sentenceList[str], required.

The word tokens.

predictionList[str], required.

The predicted BIO labels.

gold_labelsList[str], required.

The gold BIO labels.