BiMPM (Bilateral Multi-Perspective Matching) model implementation.


    text_field_embedder: allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder,
    matcher_word: allennlp.modules.bimpm_matching.BiMpmMatching,
    encoder1: allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder,
    matcher_forward1: allennlp.modules.bimpm_matching.BiMpmMatching,
    matcher_backward1: allennlp.modules.bimpm_matching.BiMpmMatching,
    encoder2: allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder,
    matcher_forward2: allennlp.modules.bimpm_matching.BiMpmMatching,
    matcher_backward2: allennlp.modules.bimpm_matching.BiMpmMatching,
    aggregator: allennlp.modules.seq2vec_encoders.seq2vec_encoder.Seq2VecEncoder,
    classifier_feedforward: allennlp.modules.feedforward.FeedForward,
    dropout: float = 0.1,
    initializer: allennlp.nn.initializers.InitializerApplicator = <allennlp.nn.initializers.InitializerApplicator object at 0x7f8af395dbe0>,
) -> None

This Model implements BiMPM model described in Bilateral Multi-Perspective Matching for Natural Language Sentences by Zhiguo Wang et al., 2017. Also please refer to the TensorFlow implementation and PyTorch implementation.

Registered as a Model with name "bimpm".


  • vocab : Vocabulary
  • text_field_embedder : TextFieldEmbedder Used to embed the premise and hypothesis TextFields we get as input to the model.
  • matcher_word : BiMpmMatching BiMPM matching on the output of word embeddings of premise and hypothesis.
  • encoder1 : Seq2SeqEncoder First encoder layer for the premise and hypothesis
  • matcher_forward1 : BiMPMMatching BiMPM matching for the forward output of first encoder layer
  • matcher_backward1 : BiMPMMatching BiMPM matching for the backward output of first encoder layer
  • encoder2 : Seq2SeqEncoder Second encoder layer for the premise and hypothesis
  • matcher_forward2 : BiMPMMatching BiMPM matching for the forward output of second encoder layer
  • matcher_backward2 : BiMPMMatching BiMPM matching for the backward output of second encoder layer
  • aggregator : Seq2VecEncoder Aggregator of all BiMPM matching vectors
  • classifier_feedforward : FeedForward Fully connected layers for classification.
  • dropout : float, optional (default=0.1) Dropout percentage to use.
  • initializer : InitializerApplicator, optional (default=InitializerApplicator()) If provided, will be used to initialize the model parameters.


    premise: Dict[str, Dict[str, torch.Tensor]],
    hypothesis: Dict[str, Dict[str, torch.Tensor]],
    label: torch.LongTensor = None,
    metadata: List[Dict[str, Any]] = None,
) -> Dict[str, torch.Tensor]


  • premise : TextFieldTensors The premise from a TextField
  • hypothesis : TextFieldTensors The hypothesis from a TextField
  • label : torch.LongTensor, optional (default = None) The label for the pair of the premise and the hypothesis
  • metadata : List[Dict[str, Any]], optional, (default = None) Additional information about the pair Returns

An output dictionary consisting of:

logits: torch.FloatTensor A tensor of shape (batch_size, num_labels) representing unnormalised log probabilities of the entailment label. loss: torch.FloatTensor, optional A scalar loss to be optimised.


BiMpm.get_metrics(self, reset:bool=False) -> Dict[str, float]

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. Metrics should be populated during the call to forward, with the Metric handling the accumulation of the metric until this method is called.


    output_dict: Dict[str, torch.Tensor],
) -> Dict[str, torch.Tensor]

Converts indices to string labels, and adds a "label" key to the result.