# -*- coding: utf-8 -*-
import torch.nn as nn
from supar.config import Config
from supar.model import Model
from supar.modules import MLP, Biaffine
[docs]class BiaffineSemanticDependencyModel(Model):
r"""
The implementation of Biaffine Semantic Dependency Parser :cite:`dozat-manning-2018-simpler`.
Args:
n_words (int):
The size of the word vocabulary.
n_labels (int):
The number of labels in the treebank.
n_tags (int):
The number of POS tags, required if POS tag embeddings are used. Default: ``None``.
n_chars (int):
The number of characters, required if character-level representations are used. Default: ``None``.
n_lemmas (int):
The number of lemmas, required if lemma embeddings are used. Default: ``None``.
encoder (str):
Encoder to use.
``'lstm'``: BiLSTM encoder.
``'bert'``: BERT-like pretrained language model (for finetuning), e.g., ``'bert-base-cased'``.
Default: ``'lstm'``.
feat (List[str]):
Additional features to use, required if ``encoder='lstm'``.
``'tag'``: POS tag embeddings.
``'char'``: Character-level representations extracted by CharLSTM.
``'lemma'``: Lemma embeddings.
``'bert'``: BERT representations, other pretrained language models like RoBERTa are also feasible.
Default: [ ``'tag'``, ``'char'``, ``'lemma'``].
n_embed (int):
The size of word embeddings. Default: 100.
n_pretrained (int):
The size of pretrained word representations. Default: 125.
n_feat_embed (int):
The size of feature representations. Default: 100.
n_char_embed (int):
The size of character embeddings serving as inputs of CharLSTM, required if using CharLSTM. Default: 50.
n_char_hidden (int):
The size of hidden states of CharLSTM, required if using CharLSTM. Default: 100.
char_pad_index (int):
The index of the padding token in the character vocabulary, required if using CharLSTM. Default: 0.
elmo (str):
Name of the pretrained ELMo registered in `ELMoEmbedding.OPTION`. Default: ``'original_5b'``.
elmo_bos_eos (Tuple[bool]):
A tuple of two boolean values indicating whether to keep start/end boundaries of elmo outputs.
Default: ``(True, False)``.
bert (str):
Specifies which kind of language model to use, e.g., ``'bert-base-cased'``.
This is required if ``encoder='bert'`` or using BERT features. The full list can be found in `transformers`_.
Default: ``None``.
n_bert_layers (int):
Specifies how many last layers to use, required if ``encoder='bert'`` or using BERT features.
The final outputs would be weighted sum of the hidden states of these layers.
Default: 4.
mix_dropout (float):
The dropout ratio of BERT layers, required if ``encoder='bert'`` or using BERT features. Default: .0.
bert_pooling (str):
Pooling way to get token embeddings.
``first``: take the first subtoken. ``last``: take the last subtoken. ``mean``: take a mean over all.
Default: ``mean``.
bert_pad_index (int):
The index of the padding token in BERT vocabulary, required if ``encoder='bert'`` or using BERT features.
Default: 0.
finetune (bool):
If ``False``, freezes all parameters, required if using pretrained layers. Default: ``False``.
n_plm_embed (int):
The size of PLM embeddings. If 0, uses the size of the pretrained embedding model. Default: 0.
embed_dropout (float):
The dropout ratio of input embeddings. Default: .2.
n_encoder_hidden (int):
The size of encoder hidden states. Default: 1200.
n_encoder_layers (int):
The number of encoder layers. Default: 3.
encoder_dropout (float):
The dropout ratio of encoder layer. Default: .33.
n_edge_mlp (int):
Edge MLP size. Default: 600.
n_label_mlp (int):
Label MLP size. Default: 600.
edge_mlp_dropout (float):
The dropout ratio of edge MLP layers. Default: .25.
label_mlp_dropout (float):
The dropout ratio of label MLP layers. Default: .33.
interpolation (int):
Constant to even out the label/edge loss. Default: .1.
pad_index (int):
The index of the padding token in the word vocabulary. Default: 0.
unk_index (int):
The index of the unknown token in the word vocabulary. Default: 1.
.. _transformers:
https://github.com/huggingface/transformers
"""
def __init__(self,
n_words,
n_labels,
n_tags=None,
n_chars=None,
n_lemmas=None,
encoder='lstm',
feat=['tag', 'char', 'lemma'],
n_embed=100,
n_pretrained=125,
n_feat_embed=100,
n_char_embed=50,
n_char_hidden=400,
char_pad_index=0,
char_dropout=0.33,
elmo='original_5b',
elmo_bos_eos=(True, False),
bert=None,
n_bert_layers=4,
mix_dropout=.0,
bert_pooling='mean',
bert_pad_index=0,
finetune=False,
n_plm_embed=0,
embed_dropout=.2,
n_encoder_hidden=1200,
n_encoder_layers=3,
encoder_dropout=.33,
n_edge_mlp=600,
n_label_mlp=600,
edge_mlp_dropout=.25,
label_mlp_dropout=.33,
interpolation=0.1,
pad_index=0,
unk_index=1,
**kwargs):
super().__init__(**Config().update(locals()))
self.edge_mlp_d = MLP(n_in=self.args.n_encoder_hidden, n_out=n_edge_mlp, dropout=edge_mlp_dropout, activation=False)
self.edge_mlp_h = MLP(n_in=self.args.n_encoder_hidden, n_out=n_edge_mlp, dropout=edge_mlp_dropout, activation=False)
self.label_mlp_d = MLP(n_in=self.args.n_encoder_hidden, n_out=n_label_mlp, dropout=label_mlp_dropout, activation=False)
self.label_mlp_h = MLP(n_in=self.args.n_encoder_hidden, n_out=n_label_mlp, dropout=label_mlp_dropout, activation=False)
self.edge_attn = Biaffine(n_in=n_edge_mlp, n_out=2, bias_x=True, bias_y=True)
self.label_attn = Biaffine(n_in=n_label_mlp, n_out=n_labels, bias_x=True, bias_y=True)
self.criterion = nn.CrossEntropyLoss()
def load_pretrained(self, embed=None):
if embed is not None:
self.pretrained = nn.Embedding.from_pretrained(embed)
if embed.shape[1] != self.args.n_pretrained:
self.embed_proj = nn.Linear(embed.shape[1], self.args.n_pretrained)
return self
[docs] def forward(self, words, feats=None):
r"""
Args:
words (~torch.LongTensor): ``[batch_size, seq_len]``.
Word indices.
feats (List[~torch.LongTensor]):
A list of feat indices.
The size is either ``[batch_size, seq_len, fix_len]`` if ``feat`` is ``'char'`` or ``'bert'``,
or ``[batch_size, seq_len]`` otherwise.
Default: ``None``.
Returns:
~torch.Tensor, ~torch.Tensor:
The first tensor of shape ``[batch_size, seq_len, seq_len, 2]`` holds scores of all possible edges.
The second of shape ``[batch_size, seq_len, seq_len, n_labels]`` holds
scores of all possible labels on each edge.
"""
x = self.encode(words, feats)
edge_d = self.edge_mlp_d(x)
edge_h = self.edge_mlp_h(x)
label_d = self.label_mlp_d(x)
label_h = self.label_mlp_h(x)
# [batch_size, seq_len, seq_len, 2]
s_edge = self.edge_attn(edge_d, edge_h).permute(0, 2, 3, 1)
# [batch_size, seq_len, seq_len, n_labels]
s_label = self.label_attn(label_d, label_h).permute(0, 2, 3, 1)
return s_edge, s_label
[docs] def loss(self, s_edge, s_label, labels, mask):
r"""
Args:
s_edge (~torch.Tensor): ``[batch_size, seq_len, seq_len, 2]``.
Scores of all possible edges.
s_label (~torch.Tensor): ``[batch_size, seq_len, seq_len, n_labels]``.
Scores of all possible labels on each edge.
labels (~torch.LongTensor): ``[batch_size, seq_len, seq_len]``.
The tensor of gold-standard labels.
mask (~torch.BoolTensor): ``[batch_size, seq_len]``.
The mask for covering the unpadded tokens.
Returns:
~torch.Tensor:
The training loss.
"""
edge_mask = labels.ge(0) & mask
edge_loss = self.criterion(s_edge[mask], edge_mask[mask].long())
label_loss = self.criterion(s_label[edge_mask], labels[edge_mask])
return self.args.interpolation * label_loss + (1 - self.args.interpolation) * edge_loss
[docs] def decode(self, s_edge, s_label):
r"""
Args:
s_edge (~torch.Tensor): ``[batch_size, seq_len, seq_len, 2]``.
Scores of all possible edges.
s_label (~torch.Tensor): ``[batch_size, seq_len, seq_len, n_labels]``.
Scores of all possible labels on each edge.
Returns:
~torch.LongTensor:
Predicted labels of shape ``[batch_size, seq_len, seq_len]``.
"""
return s_label.argmax(-1).masked_fill_(s_edge.argmax(-1).lt(1), -1)