# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from supar.config import Config
from supar.model import Model
from supar.modules import MLP, Biaffine
from supar.structs import ConstituencyCRF
[docs]class CRFConstituencyModel(Model):
r"""
The implementation of CRF Constituency Parser :cite:`zhang-etal-2020-fast`,
also called FANCY (abbr. of Fast and Accurate Neural Crf constituencY) Parser.
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``.
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.
``'bert'``: BERT representations, other pretrained language models like RoBERTa are also feasible.
Default: [``'char'``].
n_embed (int):
The size of word embeddings. Default: 100.
n_pretrained (int):
The size of pretrained word embeddings. Default: 100.
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: .33.
n_encoder_hidden (int):
The size of encoder hidden states. Default: 800.
n_encoder_layers (int):
The number of encoder layers. Default: 3.
encoder_dropout (float):
The dropout ratio of encoder layer. Default: .33.
n_span_mlp (int):
Span MLP size. Default: 500.
n_label_mlp (int):
Label MLP size. Default: 100.
mlp_dropout (float):
The dropout ratio of MLP layers. Default: .33.
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,
encoder='lstm',
feat=['char'],
n_embed=100,
n_pretrained=100,
n_feat_embed=100,
n_char_embed=50,
n_char_hidden=100,
char_pad_index=0,
elmo='original_5b',
elmo_bos_eos=(True, True),
bert=None,
n_bert_layers=4,
mix_dropout=.0,
bert_pooling='mean',
bert_pad_index=0,
finetune=False,
n_plm_embed=0,
embed_dropout=.33,
n_encoder_hidden=800,
n_encoder_layers=3,
encoder_dropout=.33,
n_span_mlp=500,
n_label_mlp=100,
mlp_dropout=.33,
pad_index=0,
unk_index=1,
**kwargs):
super().__init__(**Config().update(locals()))
self.span_mlp_l = MLP(n_in=self.args.n_encoder_hidden, n_out=n_span_mlp, dropout=mlp_dropout)
self.span_mlp_r = MLP(n_in=self.args.n_encoder_hidden, n_out=n_span_mlp, dropout=mlp_dropout)
self.label_mlp_l = MLP(n_in=self.args.n_encoder_hidden, n_out=n_label_mlp, dropout=mlp_dropout)
self.label_mlp_r = MLP(n_in=self.args.n_encoder_hidden, n_out=n_label_mlp, dropout=mlp_dropout)
self.span_attn = Biaffine(n_in=n_span_mlp, bias_x=True, bias_y=False)
self.label_attn = Biaffine(n_in=n_label_mlp, n_out=n_labels, bias_x=True, bias_y=True)
self.criterion = nn.CrossEntropyLoss()
[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]`` holds scores of all possible constituents.
The second of shape ``[batch_size, seq_len, seq_len, n_labels]`` holds
scores of all possible labels on each constituent.
"""
x = self.encode(words, feats)
x_f, x_b = x.chunk(2, -1)
x = torch.cat((x_f[:, :-1], x_b[:, 1:]), -1)
span_l = self.span_mlp_l(x)
span_r = self.span_mlp_r(x)
label_l = self.label_mlp_l(x)
label_r = self.label_mlp_r(x)
# [batch_size, seq_len, seq_len]
s_span = self.span_attn(span_l, span_r)
# [batch_size, seq_len, seq_len, n_labels]
s_label = self.label_attn(label_l, label_r).permute(0, 2, 3, 1)
return s_span, s_label
[docs] def loss(self, s_span, s_label, charts, mask, mbr=True):
r"""
Args:
s_span (~torch.Tensor): ``[batch_size, seq_len, seq_len]``.
Scores of all constituents.
s_label (~torch.Tensor): ``[batch_size, seq_len, seq_len, n_labels]``.
Scores of all constituent labels.
charts (~torch.LongTensor): ``[batch_size, seq_len, seq_len]``.
The tensor of gold-standard labels. Positions without labels are filled with -1.
mask (~torch.BoolTensor): ``[batch_size, seq_len, seq_len]``.
The mask for covering the unpadded tokens in each chart.
mbr (bool):
If ``True``, returns marginals for MBR decoding. Default: ``True``.
Returns:
~torch.Tensor, ~torch.Tensor:
The training loss and original constituent scores
of shape ``[batch_size, seq_len, seq_len]`` if ``mbr=False``, or marginals otherwise.
"""
span_mask = charts.ge(0) & mask
span_dist = ConstituencyCRF(s_span, mask[:, 0].sum(-1))
span_loss = -span_dist.log_prob(charts).sum() / mask[:, 0].sum()
span_probs = span_dist.marginals if mbr else s_span
label_loss = self.criterion(s_label[span_mask], charts[span_mask])
loss = span_loss + label_loss
return loss, span_probs
[docs] def decode(self, s_span, s_label, mask):
r"""
Args:
s_span (~torch.Tensor): ``[batch_size, seq_len, seq_len]``.
Scores of all constituents.
s_label (~torch.Tensor): ``[batch_size, seq_len, seq_len, n_labels]``.
Scores of all constituent labels.
mask (~torch.BoolTensor): ``[batch_size, seq_len, seq_len]``.
The mask for covering the unpadded tokens in each chart.
Returns:
List[List[Tuple]]:
Sequences of factorized labeled trees.
"""
span_preds = ConstituencyCRF(s_span, mask[:, 0].sum(-1)).argmax
label_preds = s_label.argmax(-1).tolist()
return [[(i, j, labels[i][j]) for i, j in spans] for spans, labels in zip(span_preds, label_preds)]