diff --git a/combo/data/dataset.py b/combo/data/dataset.py
index 459a755c7f71c40e449d0542bf9af21d05e1f2c9..0b53df3284cde5d11e95ecd496059b6f55921e35 100644
--- a/combo/data/dataset.py
+++ b/combo/data/dataset.py
@@ -1,9 +1,10 @@
import logging
-from typing import Union, List, Dict, Iterable, Optional, Any
+from typing import Union, List, Dict, Iterable, Optional, Any, Tuple
import conllu
+import torch
from allennlp import data as allen_data
-from allennlp.common import checks
+from allennlp.common import checks, util
from allennlp.data import fields as allen_fields, vocabulary
from conllu import parser
from dataclasses import dataclass
@@ -35,6 +36,9 @@ class UniversalDependenciesDatasetReader(allen_data.DatasetReader):
if "token" not in features and "char" not in features:
raise checks.ConfigurationError("There must be at least one ('char' or 'token') text-based feature!")
+ if "deps" in targets and not ("head" in targets and "deprel" in targets):
+ raise checks.ConfigurationError("Add 'head' and 'deprel' to targets when using 'deps'!")
+
intersection = set(features).intersection(set(targets))
if len(intersection) != 0:
raise checks.ConfigurationError(
@@ -102,13 +106,40 @@ class UniversalDependenciesDatasetReader(allen_data.DatasetReader):
elif target_name == "feats":
target_values = self._feat_values(tree_tokens)
fields_[target_name] = fields.SequenceMultiLabelField(target_values,
- self._feats_to_index_multi_label,
+ self._feats_indexer,
+ self._feats_as_tensor_wrapper,
text_field,
label_namespace="feats_labels")
elif target_name == "head":
target_values = [0 if v == "_" else int(v) for v in target_values]
fields_[target_name] = allen_fields.SequenceLabelField(target_values, text_field,
label_namespace=target_name + "_labels")
+ elif target_name == "deps":
+ heads = [0 if t["head"] == "_" else int(t["head"]) for t in tree_tokens]
+ deprels = [t["deprel"] for t in tree_tokens]
+ enhanced_heads: List[Tuple[int, int]] = []
+ enhanced_deprels: List[str] = []
+ for idx, t in enumerate(tree_tokens):
+ enhanced_heads.append((idx, heads[idx]))
+ enhanced_deprels.append(deprels[idx])
+ t_deps = t["deps"]
+ if t_deps and t_deps != "_":
+ t_heads, t_deprels = zip(*[tuple(d.split(":")) for d in t_deps.split("|")])
+ enhanced_heads.extend([(idx, t) for t in t_heads])
+ enhanced_deprels.extend(t_deprels)
+ fields_["enhanced_heads"] = allen_fields.AdjacencyField(
+ indices=enhanced_heads,
+ sequence_field=text_field,
+ label_namespace="enhanced_heads_labels",
+ padding_value=0,
+ )
+ fields_["enhanced_deprels"] = allen_fields.AdjacencyField(
+ indices=enhanced_heads,
+ sequence_field=text_field,
+ labels=enhanced_deprels,
+ label_namespace="enhanced_deprels_labels",
+ padding_value=0,
+ )
else:
fields_[target_name] = allen_fields.SequenceLabelField(target_values, text_field,
label_namespace=target_name + "_labels")
@@ -151,12 +182,26 @@ class UniversalDependenciesDatasetReader(allen_data.DatasetReader):
return features
@staticmethod
- def _feats_to_index_multi_label(vocab: allen_data.Vocabulary):
+ def _feats_as_tensor_wrapper(field: fields.SequenceMultiLabelField):
+ def as_tensor(padding_lengths):
+ desired_num_tokens = padding_lengths["num_tokens"]
+ assert len(field._indexed_multi_labels) > 0
+ classes_count = len(field._indexed_multi_labels[0])
+ default_value = [0.0] * classes_count
+ padded_tags = util.pad_sequence_to_length(field._indexed_multi_labels, desired_num_tokens,
+ lambda: default_value)
+ tensor = torch.LongTensor(padded_tags)
+ return tensor
+
+ return as_tensor
+
+ @staticmethod
+ def _feats_indexer(vocab: allen_data.Vocabulary):
label_namespace = "feats_labels"
vocab_size = vocab.get_vocab_size(label_namespace)
slices = get_slices_if_not_provided(vocab)
- def _m_from_n_ones_encoding(multi_label: List[str]) -> List[int]:
+ def _m_from_n_ones_encoding(multi_label: List[str], sentence_length: int) -> List[int]:
one_hot_encoding = [0] * vocab_size
for cat, cat_indices in slices.items():
if cat not in ["__PAD__", "_"]:
diff --git a/combo/data/fields/sequence_multilabel_field.py b/combo/data/fields/sequence_multilabel_field.py
index 4e98a148aee35e42af0b4828a031368fe0eafc12..b200580cf1edfba1e710b42bc19c4c4efdb0db4f 100644
--- a/combo/data/fields/sequence_multilabel_field.py
+++ b/combo/data/fields/sequence_multilabel_field.py
@@ -5,7 +5,7 @@ from typing import Set, List, Callable, Iterator, Union, Dict
import torch
from allennlp import data
-from allennlp.common import checks, util
+from allennlp.common import checks
from allennlp.data import fields
from overrides import overrides
@@ -17,15 +17,16 @@ class SequenceMultiLabelField(data.Field[torch.Tensor]):
A `SequenceMultiLabelField` is an extension of the :class:`MultiLabelField` that allows for multiple labels
while keeping sequence dimension.
- This field will get converted into a sequence of vectors of length equal to the vocabulary size with
- M from N encoding for the labels (all zeros, and ones for the labels).
+ To allow configuration to different circumstances, class takes few delegates functions.
# Parameters
multi_labels : `List[List[str]]`
multi_label_indexer : `Callable[[data.Vocabulary], Callable[[List[str]], List[int]]]`
- Nested callable which based on vocab creates mapper for multilabel field in the sequence from strings
- to indexed, int values.
+ Nested callable which based on vocab and sequence length maps values of the fields in the sequence
+ from strings to indexed, int values.
+ as_tensor: `Callable[["SequenceMultiLabelField"], Callable[[Dict[str, int]], torch.Tensor]]`
+ Nested callable which based on the field itself, maps indexed data to a tensor.
sequence_field : `SequenceField`
A field containing the sequence that this `SequenceMultiLabelField` is labeling. Most often, this is a
`TextField`, for tagging individual tokens in a sentence.
@@ -43,7 +44,8 @@ class SequenceMultiLabelField(data.Field[torch.Tensor]):
def __init__(
self,
multi_labels: List[List[str]],
- multi_label_indexer: Callable[[data.Vocabulary], Callable[[List[str]], List[int]]],
+ multi_label_indexer: Callable[[data.Vocabulary], Callable[[List[str], int], List[int]]],
+ as_tensor: Callable[["SequenceMultiLabelField"], Callable[[Dict[str, int]], torch.Tensor]],
sequence_field: fields.SequenceField,
label_namespace: str = "labels",
) -> None:
@@ -53,6 +55,7 @@ class SequenceMultiLabelField(data.Field[torch.Tensor]):
self._label_namespace = label_namespace
self._indexed_multi_labels = None
self._maybe_warn_for_namespace(label_namespace)
+ self.as_tensor_wrapper = as_tensor(self)
if len(multi_labels) != sequence_field.sequence_length():
raise checks.ConfigurationError(
"Label length and sequence length "
@@ -101,7 +104,7 @@ class SequenceMultiLabelField(data.Field[torch.Tensor]):
indexed = []
for multi_label in self.multi_labels:
- indexed.append(indexer(multi_label))
+ indexed.append(indexer(multi_label, len(self.multi_labels)))
self._indexed_multi_labels = indexed
@overrides
@@ -110,19 +113,13 @@ class SequenceMultiLabelField(data.Field[torch.Tensor]):
@overrides
def as_tensor(self, padding_lengths: Dict[str, int]) -> torch.Tensor:
- desired_num_tokens = padding_lengths["num_tokens"]
- assert len(self._indexed_multi_labels) > 0
- classes_count = len(self._indexed_multi_labels[0])
- default_value = [0.0] * classes_count
- padded_tags = util.pad_sequence_to_length(self._indexed_multi_labels, desired_num_tokens, lambda: default_value)
- tensor = torch.LongTensor(padded_tags)
- return tensor
+ return self.as_tensor_wrapper(padding_lengths)
@overrides
def empty_field(self) -> "SequenceMultiLabelField":
- # The empty_list here is needed for mypy
empty_list: List[List[str]] = [[]]
sequence_label_field = SequenceMultiLabelField(empty_list, lambda x: lambda y: y,
+ lambda x: lambda y: y,
self.sequence_field.empty_field())
sequence_label_field._indexed_labels = empty_list
return sequence_label_field
diff --git a/combo/main.py b/combo/main.py
index 44ad091f8c5004bf9f4e70323ec828bf44288447..3f7fad44a829b8d514c5c64acd486b99b644a739 100644
--- a/combo/main.py
+++ b/combo/main.py
@@ -18,7 +18,7 @@ from combo.utils import checks
logger = logging.getLogger(__name__)
_FEATURES = ["token", "char", "upostag", "xpostag", "lemma", "feats"]
-_TARGETS = ["deprel", "feats", "head", "lemma", "upostag", "xpostag", "semrel", "sent"]
+_TARGETS = ["deprel", "feats", "head", "lemma", "upostag", "xpostag", "semrel", "sent", "deps"]
FLAGS = flags.FLAGS
flags.DEFINE_enum(name="mode", default=None, enum_values=["train", "predict"],
diff --git a/combo/models/model.py b/combo/models/model.py
index 77b43e3c1a95e09b15c310409af0090f097d47fa..ec0f113be9a3c00868f5c9cce3d8f75f789bcb81 100644
--- a/combo/models/model.py
+++ b/combo/models/model.py
@@ -53,7 +53,9 @@ class SemanticMultitaskModel(allen_models.Model):
feats: torch.Tensor = None,
head: torch.Tensor = None,
deprel: torch.Tensor = None,
- semrel: torch.Tensor = None, ) -> Dict[str, torch.Tensor]:
+ semrel: torch.Tensor = None,
+ enhanced_heads: torch.Tensor = None,
+ enhanced_deprels: torch.Tensor = None) -> Dict[str, torch.Tensor]:
# Prepare masks
char_mask: torch.BoolTensor = sentence["char"]["token_characters"] > 0
diff --git a/tests/data/fields/test_sequence_multilabel_field.py b/tests/data/fields/test_sequence_multilabel_field.py
index d2a1f8bc6b4d853e427d6a7214592f1b881db52c..fff8ff4ee285215aa4015d1c55609f3c2d28a3a6 100644
--- a/tests/data/fields/test_sequence_multilabel_field.py
+++ b/tests/data/fields/test_sequence_multilabel_field.py
@@ -4,6 +4,7 @@ from typing import List
import torch
from allennlp import data as allen_data
+from allennlp.common import util
from allennlp.data import fields as allen_fields
from combo.data import fields
@@ -22,7 +23,7 @@ class IndexingSequenceMultiLabelFieldTest(unittest.TestCase):
def _indexer(vocab: allen_data.Vocabulary):
vocab_size = vocab.get_vocab_size(self.namespace)
- def _mapper(multi_label: List[str]) -> List[int]:
+ def _mapper(multi_label: List[str], _: int) -> List[int]:
one_hot = [0] * vocab_size
for label in multi_label:
index = vocab.get_token_index(label, self.namespace)
@@ -31,7 +32,21 @@ class IndexingSequenceMultiLabelFieldTest(unittest.TestCase):
return _mapper
+ def _as_tensor(field: fields.SequenceMultiLabelField):
+
+ def _wrapped(padding_lengths):
+ desired_num_tokens = padding_lengths["num_tokens"]
+ classes_count = len(field._indexed_multi_labels[0])
+ default_value = [0.0] * classes_count
+ padded_tags = util.pad_sequence_to_length(field._indexed_multi_labels, desired_num_tokens,
+ lambda: default_value)
+ tensor = torch.LongTensor(padded_tags)
+ return tensor
+
+ return _wrapped
+
self.indexer = _indexer
+ self.as_tensor = _as_tensor
self.sequence_field = _SequenceFieldTestWrapper(self.vocab.get_vocab_size(self.namespace))
def test_indexing(self):
@@ -39,6 +54,7 @@ class IndexingSequenceMultiLabelFieldTest(unittest.TestCase):
field = fields.SequenceMultiLabelField(
multi_labels=[["t1", "t2"], [], ["t0"]],
multi_label_indexer=self.indexer,
+ as_tensor=self.as_tensor,
sequence_field=self.sequence_field,
label_namespace=self.namespace
)
@@ -55,6 +71,7 @@ class IndexingSequenceMultiLabelFieldTest(unittest.TestCase):
field = fields.SequenceMultiLabelField(
multi_labels=[["t1", "t2"], [], ["t0"]],
multi_label_indexer=self.indexer,
+ as_tensor=self.as_tensor,
sequence_field=self.sequence_field,
label_namespace=self.namespace
)
@@ -72,6 +89,7 @@ class IndexingSequenceMultiLabelFieldTest(unittest.TestCase):
field = fields.SequenceMultiLabelField(
multi_labels=[["t1", "t2"], [], ["t0"]],
multi_label_indexer=self.indexer,
+ as_tensor=self.as_tensor,
sequence_field=self.sequence_field,
label_namespace=self.namespace
)