Add support of tokenized input for coref and srl predictors

.gitignore

@@ -11,6 +11,7 @@ dist/
 
 .envrc
 .python-version
+.idea
 
 
 # jupyter notebooks

allennlp/data/tokenizers/word_splitter.py

@@ -131,6 +131,7 @@ def split_words(self, sentence: str) -> List[Token]:
 def _remove_spaces(tokens: List[spacy.tokens.Token]) -> List[spacy.tokens.Token]:
     return [token for token in tokens if not token.is_space]
 
+
 @WordSplitter.register('spacy')
 class SpacyWordSplitter(WordSplitter):
     """
@@ -154,6 +155,7 @@ def split_words(self, sentence: str) -> List[Token]:
         # This works because our Token class matches spacy's.
         return _remove_spaces(self.spacy(sentence))
 
+
 @WordSplitter.register('openai')
 class OpenAISplitter(WordSplitter):
     """

allennlp/predictors/coref.py

@@ -1,7 +1,9 @@
+from typing import List
+
 from overrides import overrides
 
-from allennlp.common.util import get_spacy_model
 from allennlp.common.util import JsonDict
+from allennlp.common.util import get_spacy_model
 from allennlp.data import DatasetReader, Instance
 from allennlp.models import Model
 from allennlp.predictors.predictor import Predictor
@@ -53,6 +55,35 @@ def predict(self, document: str) -> JsonDict:
         """
         return self.predict_json({"document" : document})
 
+    def predict_tokenized(self, tokenized_document: List[str]) -> JsonDict:
+        """
+        Predict the coreference clusters in the given document.
+
+        Parameters
+        ----------
+        tokenized_document : ``List[str]``
+            A list of words representation of a tokenized document.
+
+        Returns
+        -------
+        A dictionary representation of the predicted coreference clusters.
+        """
+        instance = self._words_list_to_instance(tokenized_document)
+        return self.predict_instance(instance)
+
+    def _words_list_to_instance(self, words: List[str]) -> Instance:
+        """
+        Create an instance from words list represent an already tokenized document,
+        for skipping tokenization when that information already exist for the user
+        """
+        spacy_document = self._spacy.tokenizer.tokens_from_list(words)
+        for pipe in filter(None, self._spacy.pipeline):
+            pipe[1](spacy_document)
+
+        sentences = [[token.text for token in sentence] for sentence in spacy_document.sents]
+        instance = self._dataset_reader.text_to_instance(sentences)
+        return instance
+
     @overrides
     def _json_to_instance(self, json_dict: JsonDict) -> Instance:
         """

allennlp/predictors/semantic_role_labeler.py

@@ -41,8 +41,33 @@ def predict(self, sentence: str) -> JsonDict:
         -------
         A dictionary representation of the semantic roles in the sentence.
         """
-        return self.predict_json({"sentence" : sentence})
+        return self.predict_json({"sentence": sentence})
 
+    def predict_tokenized(self, tokenized_sentence: List[str]) -> JsonDict:
+        """
+        Predicts the semantic roles of the supplied sentence tokens and returns a dictionary
+        with the results.
+
+        Parameters
+        ----------
+        tokenized_sentence, ``List[str]``
+            The sentence tokens to parse via semantic role labeling.
+
+        Returns
+        -------
+        A dictionary representation of the semantic roles in the sentence.
+        """
+        spacy_doc = self._tokenizer.spacy.tokenizer.tokens_from_list(tokenized_sentence)
+        for pipe in filter(None, self._tokenizer.spacy.pipeline):
+            pipe[1](spacy_doc)
+
+        tokens = [token for token in spacy_doc]
+        instances = self.tokens_to_instances(tokens)
+
+        if not instances:
+            return sanitize({"verbs": [], "words": tokens})
+
+        return self.predict_instances(instances)
 
     @staticmethod
     def make_srl_string(words: List[str], tags: List[str]) -> str:
@@ -71,6 +96,17 @@ def make_srl_string(words: List[str], tags: List[str]) -> str:
     def _json_to_instance(self, json_dict: JsonDict):
         raise NotImplementedError("The SRL model uses a different API for creating instances.")
 
+    def tokens_to_instances(self, tokens):
+        words = [token.text for token in tokens]
+        instances: List[Instance] = []
+        for i, word in enumerate(tokens):
+            if word.pos_ == "VERB":
+                verb_labels = [0 for _ in words]
+                verb_labels[i] = 1
+                instance = self._dataset_reader.text_to_instance(tokens, verb_labels)
+                instances.append(instance)
+        return instances
+
     def _sentence_to_srl_instances(self, json_dict: JsonDict) -> List[Instance]:
         """
         The SRL model has a slightly different API from other models, as the model is run
@@ -92,15 +128,7 @@ def _sentence_to_srl_instances(self, json_dict: JsonDict) -> List[Instance]:
         """
         sentence = json_dict["sentence"]
         tokens = self._tokenizer.split_words(sentence)
-        words = [token.text for token in tokens]
-        instances: List[Instance] = []
-        for i, word in enumerate(tokens):
-            if word.pos_ == "VERB":
-                verb_labels = [0 for _ in words]
-                verb_labels[i] = 1
-                instance = self._dataset_reader.text_to_instance(tokens, verb_labels)
-                instances.append(instance)
-        return instances
+        return self.tokens_to_instances(tokens)
 
     @overrides
     def predict_batch_json(self, inputs: List[JsonDict]) -> List[JsonDict]:
@@ -178,6 +206,21 @@ def predict_batch_json(self, inputs: List[JsonDict]) -> List[JsonDict]:
 
         return sanitize(return_dicts)
 
+    def predict_instances(self, instances: List[Instance]) -> JsonDict:
+        outputs = self._model.forward_on_instances(instances)
+
+        results = {"verbs": [], "words": outputs[0]["words"]}
+        for output in outputs:
+            tags = output['tags']
+            description = self.make_srl_string(output["words"], tags)
+            results["verbs"].append({
+                    "verb": output["verb"],
+                    "description": description,
+                    "tags": tags,
+            })
+
+        return sanitize(results)
+
     @overrides
     def predict_json(self, inputs: JsonDict) -> JsonDict:
         """
@@ -198,16 +241,4 @@ def predict_json(self, inputs: JsonDict) -> JsonDict:
         if not instances:
             return sanitize({"verbs": [], "words": self._tokenizer.split_words(inputs["sentence"])})
 
-        outputs = self._model.forward_on_instances(instances)
-
-        results = {"verbs": [], "words": outputs[0]["words"]}
-        for output in outputs:
-            tags = output['tags']
-            description = self.make_srl_string(output["words"], tags)
-            results["verbs"].append({
-                    "verb": output["verb"],
-                    "description": description,
-                    "tags": tags,
-            })
-
-        return sanitize(results)
+        return self.predict_instances(instances)

allennlp/tests/predictors/coref_test.py

@@ -13,12 +13,21 @@ def test_uses_named_inputs(self):
         predictor = Predictor.from_archive(archive, 'coreference-resolution')
 
         result = predictor.predict_json(inputs)
+        self.assert_predict_result(result)
 
+        document = ['This', 'is', 'a', 'single', 'string',
+                    'document', 'about', 'a', 'test', '.', 'Sometimes',
+                    'it', 'contains', 'coreferent', 'parts', '.']
+
+        result_doc_words = predictor.predict_tokenized(document)
+        self.assert_predict_result(result_doc_words)
+
+    @staticmethod
+    def assert_predict_result(result):
         document = result["document"]
         assert document == ['This', 'is', 'a', 'single', 'string',
                             'document', 'about', 'a', 'test', '.', 'Sometimes',
                             'it', 'contains', 'coreferent', 'parts', '.']
-
         clusters = result["clusters"]
         assert isinstance(clusters, list)
         for cluster in clusters:

allennlp/tests/predictors/srl_test.py

@@ -13,21 +13,28 @@ def test_uses_named_inputs(self):
         archive = load_archive(self.FIXTURES_ROOT / 'srl' / 'serialization' / 'model.tar.gz')
         predictor = Predictor.from_archive(archive, 'semantic-role-labeling')
 
-        result = predictor.predict_json(inputs)
+        result_json = predictor.predict_json(inputs)
+        self.assert_predict_result(result_json)
 
+        words = ["The", "squirrel", "wrote", "a", "unit", "test",
+                 "to", "make", "sure", "its", "nuts", "worked", "as", "designed", "."]
+
+        result_words = predictor.predict_tokenized(words)
+        self.assert_predict_result(result_words)
+
+    @staticmethod
+    def assert_predict_result(result):
         words = result.get("words")
         assert words == ["The", "squirrel", "wrote", "a", "unit", "test",
                          "to", "make", "sure", "its", "nuts", "worked", "as", "designed", "."]
-        num_words = len(words)
 
+        num_words = len(words)
         verbs = result.get("verbs")
         assert verbs is not None
         assert isinstance(verbs, list)
-
         assert any(v["verb"] == "wrote" for v in verbs)
         assert any(v["verb"] == "make" for v in verbs)
         assert any(v["verb"] == "worked" for v in verbs)
-
         for verb in verbs:
             tags = verb.get("tags")
             assert tags is not None