BidirectionalEndpointSpanExtractor(self, input_dim:int, forward_combination:str='y-x', backward_combination:str='x-y', num_width_embeddings:int=None, span_width_embedding_dim:int=None, bucket_widths:bool=False, use_sentinels:bool=True) -> None
Represents spans from a bidirectional encoder as a concatenation of two different representations of the span endpoints, one for the forward direction of the encoder and one from the backward direction. This type of representation encodes some subtlety, because when you consider the forward and backward directions separately, the end index of the span for the backward direction's representation is actually the start index.
By default, this
SpanExtractor represents spans as
sequence_tensor[inclusive_span_end] - sequence_tensor[exclusive_span_start]
meaning that the representation is the difference between the the last word in the span
and the word
before the span started. Note that the start and end indices are with
respect to the direction that the RNN is going in, so for the backward direction, the
start/end indices are reversed.
Additionally, the width of the spans can be embedded and concatenated on to the final combination.
The following other types of representation are supported for both the forward and backward
directions, assuming that
x = span_start_embeddings and
y = span_end_embeddings.
x/y, where each of those binary operations
is performed elementwise. You can list as many combinations as you want, comma separated.
For example, you might give
x,y,x*y as the
combination parameter to this class.
The computed similarity function would then be
[x; y; x*y], which can then be optionally
concatenated with an embedded representation of the width of the span.
- input_dim :
int, required The final dimension of the
- forward_combination :
str, optional (default = "y-x"). The method used to combine the
forward_end_embeddingsfor the forward direction of the bidirectional representation. See above for a full description.
- backward_combination :
str, optional (default = "x-y"). The method used to combine the
backward_end_embeddingsfor the backward direction of the bidirectional representation. See above for a full description.
- num_width_embeddings :
int, optional (default = None). Specifies the number of buckets to use when representing span width features.
- span_width_embedding_dim :
int, optional (default = None). The embedding size for the span_width features.
- bucket_widths :
bool, optional (default = False). Whether to bucket the span widths into log-space buckets. If
False, the raw span widths are used.
- use_sentinels :
bool, optional (default =
True, sentinels are used to represent exclusive span indices for the elements in the first and last positions in the sequence (as the exclusive indices for these elements are outside of the the sequence boundary). This is not strictly necessary, as you may know that your exclusive start and end indices are always within your sequence representation, such as if you have appended/prepended
and tokens to your sequence.
BidirectionalEndpointSpanExtractor.forward(self, sequence_tensor:torch.FloatTensor, span_indices:torch.LongTensor, sequence_mask:torch.LongTensor=None, span_indices_mask:torch.LongTensor=None) -> torch.FloatTensor
Given a sequence tensor, extract spans and return representations of them. Span representation can be computed in many different ways, such as concatenation of the start and end spans, attention over the vectors contained inside the span, etc.
- sequence_tensor :
torch.FloatTensor, required. A tensor of shape (batch_size, sequence_length, embedding_size) representing an embedded sequence of words.
- span_indices :
torch.LongTensor, required. A tensor of shape
(batch_size, num_spans, 2), where the last dimension represents the inclusive start and end indices of the span to be extracted from the
- sequence_mask :
torch.LongTensor, optional (default =
None). A tensor of shape (batch_size, sequence_length) representing padded elements of the sequence.
- span_indices_mask :
torch.LongTensor, optional (default =
None). A tensor of shape (batch_size, num_spans) representing the valid spans in the
indicestensor. This mask is optional because sometimes it's easier to worry about masking after calling this function, rather than passing a mask directly.
A tensor of shape
(batch_size, num_spans, embedded_span_size),
embedded_span_size depends on the way spans are represented.