bimpm_matching

allennlp.modules.bimpm_matching

[SOURCE]

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Multi-perspective matching layer

multi_perspective_match

def multi_perspective_match(
    vector1: torch.Tensor,
    vector2: torch.Tensor,
    weight: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]

Calculate multi-perspective cosine matching between time-steps of vectors of the same length.

Parameters

• vector1 : torch.Tensor
  A tensor of shape (batch, seq_len, hidden_size)
• vector2 : torch.Tensor
  A tensor of shape (batch, seq_len or 1, hidden_size)
• weight : torch.Tensor
  A tensor of shape (num_perspectives, hidden_size)

Returns

• torch.Tensor :
  Shape (batch, seq_len, 1).
• torch.Tensor :
  Shape (batch, seq_len, num_perspectives).

multi_perspective_match_pairwise

def multi_perspective_match_pairwise(
    vector1: torch.Tensor,
    vector2: torch.Tensor,
    weight: torch.Tensor
) -> torch.Tensor

Calculate multi-perspective cosine matching between each time step of one vector and each time step of another vector.

Parameters

• vector1 : torch.Tensor
  A tensor of shape (batch, seq_len1, hidden_size)
• vector2 : torch.Tensor
  A tensor of shape (batch, seq_len2, hidden_size)
• weight : torch.Tensor
  A tensor of shape (num_perspectives, hidden_size)

Returns

• torch.Tensor :
  A tensor of shape (batch, seq_len1, seq_len2, num_perspectives) consisting multi-perspective matching results

BiMpmMatching

class BiMpmMatching(nn.Module,  FromParams):
 | def __init__(
 |     self,
 |     hidden_dim: int = 100,
 |     num_perspectives: int = 20,
 |     share_weights_between_directions: bool = True,
 |     is_forward: bool = None,
 |     with_full_match: bool = True,
 |     with_maxpool_match: bool = True,
 |     with_attentive_match: bool = True,
 |     with_max_attentive_match: bool = True
 | ) -> None

This Module implements the matching layer of 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.

Parameters

• hidden_dim : int, optional (default = 100)
  The hidden dimension of the representations
• num_perspectives : int, optional (default = 20)
  The number of perspectives for matching
• share_weights_between_directions : bool, optional (default = True)
  If True, share weight between matching from sentence1 to sentence2 and from sentence2 to sentence1, useful for non-symmetric tasks
• is_forward : bool, optional (default = None)
  Whether the matching is for forward sequence or backward sequence, useful in finding last token in full matching. It can not be None if with_full_match is True.
• with_full_match : bool, optional (default = True)
  If True, include full match
• with_maxpool_match : bool, optional (default = True)
  If True, include max pool match
• with_attentive_match : bool, optional (default = True)
  If True, include attentive match
• with_max_attentive_match : bool, optional (default = True)
  If True, include max attentive match

get_output_dim

class BiMpmMatching(nn.Module,  FromParams):
 | ...
 | def get_output_dim(self) -> int

forward

class BiMpmMatching(nn.Module,  FromParams):
 | ...
 | def forward(
 |     self,
 |     context_1: torch.Tensor,
 |     mask_1: torch.BoolTensor,
 |     context_2: torch.Tensor,
 |     mask_2: torch.BoolTensor
 | ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]

Given the forward (or backward) representations of sentence1 and sentence2, apply four bilateral matching functions between them in one direction.

Parameters

• context_1 : torch.Tensor
  Tensor of shape (batch_size, seq_len1, hidden_dim) representing the encoding of the first sentence.
• mask_1 : torch.BoolTensor
  Boolean Tensor of shape (batch_size, seq_len1), indicating which positions in the first sentence are padding (0) and which are not (1).
• context_2 : torch.Tensor
  Tensor of shape (batch_size, seq_len2, hidden_dim) representing the encoding of the second sentence.
• mask_2 : torch.BoolTensor
  Boolean Tensor of shape (batch_size, seq_len2), indicating which positions in the second sentence are padding (0) and which are not (1).

Returns

• Tuple[List[torch.Tensor], List[torch.Tensor]] :
  A tuple of matching vectors for the two sentences. Each of which is a list of matching vectors of shape (batch, seq_len, num_perspectives or 1)