Contents

Constituency Models

CRFConstituencyModel

class supar.models.const.CRFConstituencyModel(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=0.0, bert_pooling='mean', bert_pad_index=0, freeze=True, embed_dropout=0.33, n_lstm_hidden=400, n_lstm_layers=3, encoder_dropout=0.33, n_span_mlp=500, n_label_mlp=100, mlp_dropout=0.33, pad_index=0, unk_index=1, **kwargs)[source]

The implementation of CRF Constituency Parser [Zhang et al. 2020b], also called FANCY (abbr. of Fast and Accurate Neural Crf constituencY) Parser.

Parameters

  • 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.

  • freeze (bool) – If True, freezes BERT parameters, required if using BERT features. Default: True.

  • embed_dropout (float) – The dropout ratio of input embeddings. Default: .33.

  • n_lstm_hidden (int) – The size of LSTM hidden states. Default: 400.

  • n_lstm_layers (int) – The number of LSTM 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.

forward(words, feats=None)[source]
Parameters

  • words (LongTensor) – [batch_size, seq_len]. Word indices.

  • feats (list[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

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.

Return type

Tensor, Tensor

loss(s_span, s_label, charts, mask, mbr=True)[source]
Parameters

  • s_span (Tensor) – [batch_size, seq_len, seq_len]. Scores of all constituents.

  • s_label (Tensor) – [batch_size, seq_len, seq_len, n_labels]. Scores of all constituent labels.

  • charts (LongTensor) – [batch_size, seq_len, seq_len]. The tensor of gold-standard labels. Positions without labels are filled with -1.

  • mask (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

The training loss and original constituent scores of shape [batch_size, seq_len, seq_len] if mbr=False, or marginals otherwise.

Return type

Tensor, Tensor

decode(s_span, s_label, mask)[source]
Parameters

  • s_span (Tensor) – [batch_size, seq_len, seq_len]. Scores of all constituents.

  • s_label (Tensor) – [batch_size, seq_len, seq_len, n_labels]. Scores of all constituent labels.

  • mask (BoolTensor) – [batch_size, seq_len, seq_len]. The mask for covering the unpadded tokens in each chart.

Returns

Sequences of factorized labeled trees traversed in pre-order.

Return type

list[list[tuple]]

VIConstituencyModel

class supar.models.const.VIConstituencyModel(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=0.0, bert_pooling='mean', bert_pad_index=0, freeze=True, embed_dropout=0.33, n_lstm_hidden=400, n_lstm_layers=3, encoder_dropout=0.33, n_span_mlp=500, n_pair_mlp=100, n_label_mlp=100, mlp_dropout=0.33, inference='mfvi', max_iter=3, interpolation=0.1, pad_index=0, unk_index=1, **kwargs)[source]

The implementation of Constituency Parser using variational inference.

Parameters

  • 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.

  • freeze (bool) – If True, freezes BERT parameters, required if using BERT features. Default: True.

  • embed_dropout (float) – The dropout ratio of input embeddings. Default: .33.

  • n_lstm_hidden (int) – The size of LSTM hidden states. Default: 400.

  • n_lstm_layers (int) – The number of LSTM layers. Default: 3.

  • encoder_dropout (float) – The dropout ratio of encoder layer. Default: .33.

  • n_span_mlp (int) – Span MLP size. Default: 500.

  • n_pair_mlp (int) – Binary factor MLP size. Default: 100.

  • n_label_mlp (int) – Label MLP size. Default: 100.

  • mlp_dropout (float) – The dropout ratio of MLP layers. Default: .33.

  • inference (str) – Approximate inference methods. Default: mfvi.

  • max_iter (int) – Max iteration times for inference. Default: 3.

  • 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.

forward(words, feats)[source]
Parameters

  • words (LongTensor) – [batch_size, seq_len]. Word indices.

  • feats (list[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.

Returns

Scores of all possible constituents ([batch_size, seq_len, seq_len]), second-order triples ([batch_size, seq_len, seq_len, n_labels]) and all possible labels on each constituent ([batch_size, seq_len, seq_len, n_labels]).

Return type

Tensor, Tensor, Tensor

loss(s_span, s_pair, s_label, charts, mask)[source]
Parameters

  • s_span (Tensor) – [batch_size, seq_len, seq_len]. Scores of all constituents.

  • s_pair (Tensor) – [batch_size, seq_len, seq_len, seq_len]. Scores of second-order triples.

  • s_label (Tensor) – [batch_size, seq_len, seq_len, n_labels]. Scores of all constituent labels.

  • charts (LongTensor) – [batch_size, seq_len, seq_len]. The tensor of gold-standard labels. Positions without labels are filled with -1.

  • mask (BoolTensor) – [batch_size, seq_len, seq_len]. The mask for covering the unpadded tokens in each chart.

Returns

The training loss and marginals of shape [batch_size, seq_len, seq_len].

Return type

Tensor, Tensor

decode(s_span, s_label, mask)[source]
Parameters

  • s_span (Tensor) – [batch_size, seq_len, seq_len]. Scores of all constituents.

  • s_label (Tensor) – [batch_size, seq_len, seq_len, n_labels]. Scores of all constituent labels.

  • mask (BoolTensor) – [batch_size, seq_len, seq_len]. The mask for covering the unpadded tokens in each chart.

Returns

Sequences of factorized labeled trees traversed in pre-order.

Return type

list[list[tuple]]

previous

Dependency Models

next

Semantic Dependency Models