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Variational Inference

DependencyMFVI

class supar.structs.vi.DependencyMFVI(max_iter=3)[source]

Mean Field Variational Inference for approximately calculating marginals of dependency trees [Wang & Tu 2020].

forward(scores, mask, target=None)[source]
Parameters

  • scores (Tensor, Tensor) – Tuple of three tensors s_arc and s_sib. s_arc ([batch_size, seq_len, seq_len]) holds scores of all possible dependent-head pairs. s_sib ([batch_size, seq_len, seq_len, seq_len]) holds the scores of dependent-head-sibling triples.

  • mask (BoolTensor) – [batch_size, seq_len]. The mask to avoid aggregation on padding tokens.

  • target (LongTensor) – [batch_size, seq_len]. A Tensor of gold-standard dependent-head pairs. Default: None.

Returns

The first is the training loss averaged by the number of tokens, which won’t be returned if target=None. The second is a tensor for marginals of shape [batch_size, seq_len, seq_len].

Return type

Tensor, Tensor

DependencyLBP

class supar.structs.vi.DependencyLBP(max_iter=3)[source]

Loopy Belief Propagation for approximately calculating marginals of dependency trees [Smith & Eisner 2008].

forward(scores, mask, target=None)[source]
Parameters

  • scores (Tensor, Tensor) – Tuple of three tensors s_arc and s_sib. s_arc ([batch_size, seq_len, seq_len]) holds scores of all possible dependent-head pairs. s_sib ([batch_size, seq_len, seq_len, seq_len]) holds the scores of dependent-head-sibling triples.

  • mask (BoolTensor) – [batch_size, seq_len]. The mask to avoid aggregation on padding tokens.

  • target (LongTensor) – [batch_size, seq_len]. A Tensor of gold-standard dependent-head pairs. Default: None.

Returns

The first is the training loss averaged by the number of tokens, which won’t be returned if target=None. The second is a tensor for marginals of shape [batch_size, seq_len, seq_len].

Return type

Tensor, Tensor

ConstituencyMFVI

class supar.structs.vi.ConstituencyMFVI(max_iter=3)[source]

Mean Field Variational Inference for approximately calculating marginals of constituent trees.

forward(scores, mask, target=None)[source]
Parameters

  • scores (Tensor, Tensor) – Tuple of two tensors s_span and s_pair. s_span ([batch_size, seq_len, seq_len]) holds scores of all possible spans. s_pair ([batch_size, seq_len, seq_len, seq_len]) holds the scores of second-order triples.

  • mask (BoolTensor) – [batch_size, seq_len, seq_len]. The mask to avoid aggregation on padding tokens.

  • target (BoolTensor) – [batch_size, seq_len, seq_len]. A Tensor of gold-standard dependent-head pairs. Default: None.

Returns

The first is the training loss averaged by the number of tokens, which won’t be returned if target=None. The second is a tensor for marginals of shape [batch_size, seq_len, seq_len].

Return type

Tensor, Tensor

ConstituencyLBP

class supar.structs.vi.ConstituencyLBP(max_iter=3)[source]

Loopy Belief Propagation for approximately calculating marginals of constituent trees.

forward(scores, mask, target=None)[source]
Parameters

  • scores (Tensor, Tensor) – Tuple of four tensors s_edge, s_sib, s_cop and s_grd. s_span ([batch_size, seq_len, seq_len]) holds scores of all possible spans. s_pair ([batch_size, seq_len, seq_len, seq_len]) holds the scores of second-order triples.

  • mask (BoolTensor) – [batch_size, seq_len, seq_len]. The mask to avoid aggregation on padding tokens.

  • target (BoolTensor) – [batch_size, seq_len, seq_len]. A Tensor of gold-standard dependent-head pairs. Default: None.

Returns

The first is the training loss averaged by the number of tokens, which won’t be returned if target=None. The second is a tensor for marginals of shape [batch_size, seq_len, seq_len].

Return type

Tensor, Tensor

SemanticDependencyMFVI

class supar.structs.vi.SemanticDependencyMFVI(max_iter=3)[source]

Mean Field Variational Inference for approximately calculating marginals of semantic dependency trees [Wang et al. 2019].

forward(scores, mask, target=None)[source]
Parameters

  • scores (Tensor, Tensor) – Tuple of four tensors s_edge, s_sib, s_cop and s_grd. s_edge ([batch_size, seq_len, seq_len]) holds scores of all possible dependent-head pairs. s_sib ([batch_size, seq_len, seq_len, seq_len]) holds the scores of dependent-head-sibling triples. s_cop ([batch_size, seq_len, seq_len, seq_len]) holds the scores of dependent-head-coparent triples. s_grd ([batch_size, seq_len, seq_len, seq_len]) holds the scores of dependent-head-grandparent triples.

  • mask (BoolTensor) – [batch_size, seq_len, seq_len]. The mask to avoid aggregation on padding tokens.

  • target (LongTensor) – [batch_size, seq_len, seq_len]. A Tensor of gold-standard dependent-head pairs. Default: None.

Returns

The first is the training loss averaged by the number of tokens, which won’t be returned if target=None. The second is a tensor for marginals of shape [batch_size, seq_len, seq_len].

Return type

Tensor, Tensor

SemanticDependencyLBP

class supar.structs.vi.SemanticDependencyLBP(max_iter=3)[source]

Loopy Belief Propagation for approximately calculating marginals of semantic dependency trees [Wang et al. 2019].

forward(scores, mask, target=None)[source]
Parameters

  • scores (Tensor, Tensor) – Tuple of four tensors s_edge, s_sib, s_cop and s_grd. s_edge ([batch_size, seq_len, seq_len]) holds scores of all possible dependent-head pairs. s_sib ([batch_size, seq_len, seq_len, seq_len]) holds the scores of dependent-head-sibling triples. s_cop ([batch_size, seq_len, seq_len, seq_len]) holds the scores of dependent-head-coparent triples. s_grd ([batch_size, seq_len, seq_len, seq_len]) holds the scores of dependent-head-grandparent triples.

  • mask (BoolTensor) – [batch_size, seq_len, seq_len]. The mask to avoid aggregation on padding tokens.

  • target (LongTensor) – [batch_size, seq_len, seq_len]. A Tensor of gold-standard dependent-head pairs. Default: None.

Returns

The first is the training loss averaged by the number of tokens, which won’t be returned if target=None. The second is a tensor for marginals of shape [batch_size, seq_len, seq_len].

Return type

Tensor, Tensor

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