Load from reformatted data (jsonl, at qasrl.org) by default - aligned question slots with qasrl-2018 data

qa_srl2020.py

@@ -16,9 +16,13 @@
 
 
 import datasets
+from dataclasses import dataclass
 from pathlib import Path
-from typing import List
+from typing import List, Tuple
 import pandas as pd
+import json
+import gzip
+import itertools
 
 
 _CITATION = """\
@@ -65,39 +69,57 @@ SOFTWARE."""
 
 
 _URLs = {
-    "sentences": {
-        "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.dev.full.csv",
-        "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.test.full.csv",
-        "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.dev.full.csv",
-        "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.test.full.csv",
+    "csv": {
+        "sentences": {
+            "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.dev.full.csv",
+            "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.test.full.csv",
+            "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.dev.full.csv",
+            "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.test.full.csv",
+        },
+        "qasrl-annotations": {
+            "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.dev.gold.csv",
+            "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.test.gold.csv",
+            "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.dev.gold.csv",
+            "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.test.gold.csv",
+        }, 
     },
-    "qasrl-annotations": {
-        "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.dev.gold.csv",
-        "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.test.gold.csv",
-        "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.dev.gold.csv",
-        "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.test.gold.csv",
-    },        
+    "jsonl": "https://qasrl.org/data/qasrl-gs.tar"       
 }
 
 SpanFeatureType = datasets.Sequence(datasets.Value("int32"), length=2)
 
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+@dataclass
+class QASRL2020BuilderConfig(datasets.BuilderConfig):
+    """ Allow the loader to re-distribute the original dev and test splits between train, dev and test. """
+    load_from: str = "jsonl" # "csv" or "jsonl"
+ 
+
 class QaSrl2020(datasets.GeneratorBasedBuilder):
     """QA-SRL2020: Question-Answer driven SRL gold-standard dataset.
     Notice: This dataset genrally follows the format of `qa_srl` and `kleinay\qa_srl2018` datasets. 
     However, it extends Features to include "is_verbal" and "verb_form" fields, as in the `kleinay\qanom` dataset that accounts for nominalizations.
     Nevertheless these fields can be ignored, since for all data points in QASRL-2020, "is_verbal"==True and "verb_form" is equivalent to the "predicate" feature.  """
 
-    VERSION = datasets.Version("1.0.0")
+    VERSION = datasets.Version("1.1.0")
+    
+    BUILDER_CONFIG_CLASS = QASRL2020BuilderConfig
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text", version=VERSION, description="This provides the QASRL-2020 (QASRL-GS) dataset"
+        QASRL2020BuilderConfig(
+            name="default", version=VERSION, description="This provides the QASRL-2020 (QASRL-GS) dataset"
+        ),
+        QASRL2020BuilderConfig(
+            name="csv", version=VERSION, description="This provides the QASRL-2020 (QASRL-GS) dataset",
+            load_from="csv"
+        ),
+        QASRL2020BuilderConfig(
+            name="jsonl", version=VERSION, description="This provides the QASRL-2020 (QASRL-GS) dataset",
+            load_from="jsonl"
         ),
     ]
 
     DEFAULT_CONFIG_NAME = (
-        "plain_text"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+        "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
     )
 
     def _info(self):
@@ -145,45 +167,124 @@ class QaSrl2020(datasets.GeneratorBasedBuilder):
                 
     def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
         """Returns SplitGenerators."""            
+        assert self.config.load_from in ("csv", "jsonl")
+
+        if self.config.load_from == "csv":
+            # prepare wiktionary for verb inflections inside 'self.verb_inflections'
+            self._prepare_wiktionary_verb_inflections(dl_manager)
+            
+            # Download and prepare all files - keep same structure as _URLs 
+            URLs = _URLs["csv"]
+            corpora = {data_type: {       
+                    section:  Path(dl_manager.download_and_extract(URLs[data_type][section]))
+                    for section in URLs[data_type] } 
+                for data_type in URLs
+            }
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "qasrl_annotations_paths": [corpora["qasrl-annotations"]["wikinews.dev"], 
+                                                    corpora["qasrl-annotations"]["wikipedia.dev"]],
+                        "sentences_paths": [corpora["sentences"]["wikinews.dev"],
+                                            corpora["sentences"]["wikipedia.dev"]],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "qasrl_annotations_paths": [corpora["qasrl-annotations"]["wikinews.test"], 
+                                                    corpora["qasrl-annotations"]["wikipedia.test"]],
+                        "sentences_paths": [corpora["sentences"]["wikinews.test"],
+                                            corpora["sentences"]["wikipedia.test"]],
+                    },
+                ),
+            ]
         
-        # prepare wiktionary for verb inflections inside 'self.verb_inflections'
-        self._prepare_wiktionary_verb_inflections(dl_manager)
-        
-        # Download and prepare all files - keep same structure as _URLs 
-        corpora = {data_type: {       
-                section:  Path(dl_manager.download_and_extract(_URLs[data_type][section]))
-                for section in _URLs[data_type] } 
-            for data_type in _URLs
-        }
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "qasrl_annotations_paths": [corpora["qasrl-annotations"]["wikinews.dev"], 
-                                                corpora["qasrl-annotations"]["wikipedia.dev"]],
-                    "sentences_paths": [corpora["sentences"]["wikinews.dev"],
-                                        corpora["sentences"]["wikipedia.dev"]],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "qasrl_annotations_paths": [corpora["qasrl-annotations"]["wikinews.test"], 
-                                                corpora["qasrl-annotations"]["wikipedia.test"]],
-                    "sentences_paths": [corpora["sentences"]["wikinews.test"],
-                                        corpora["sentences"]["wikipedia.test"]],
-                },
-            ),
-        ]
+        elif self.config.load_from == "jsonl":
+            self.corpus_base_path = Path(dl_manager.download_and_extract(_URLs["jsonl"]))        
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "qasrl_annotations_paths": self.corpus_base_path / "qasrl-gs" / "dev.jsonl.gz",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "qasrl_annotations_paths": self.corpus_base_path / "qasrl-gs" / "test.jsonl.gz",
+                    },
+                ),
+            ]
+   
 
+    def _generate_examples(self, qasrl_annotations_paths: List[str], sentences_paths: List[str] = None):
+        if self.config.load_from == "csv":
+            return self._generate_examples_from_csv(qasrl_annotations_paths, sentences_paths)
+        elif self.config.load_from == "jsonl": 
+            return self._generate_examples_from_jsonl(qasrl_annotations_paths)
+
+    def _generate_examples_from_jsonl(self, qasrl_annotations_path):
+        """ Yields examples from a jsonl.gz file, in same format as qasrl-v2."""
+        empty_to_underscore = lambda s: "_" if s=="" else s
+        with gzip.open(qasrl_annotations_path, "rt") as f:
+            qa_counter = 0
+            for line in f:
+                sent_obj = json.loads(line.strip())
+                tokens = sent_obj['sentenceTokens']
+                sentence = ' '.join(tokens)
+                for predicate_idx, verb_obj in sent_obj['verbEntries'].items():
+                    verb_forms = verb_obj['verbInflectedForms']
+                    predicate = tokens[int(predicate_idx)]
+                    for question_obj in verb_obj['questionLabels'].values():
+                        question_slots = question_obj['questionSlots']
+                        verb_form = question_slots['verb'] 
+                        verb_surface = verb_forms[verb_form.split(" ")[-1]] # if verb_form in verb_forms else verb_forms['stem']
+                        question_slots_in_order = [
+                            question_slots["wh"],
+                            question_slots["aux"],
+                            question_slots["subj"],                            
+                            verb_surface,
+                            question_slots["obj"],
+                            empty_to_underscore(question_slots["prep"]), # fix bug in data
+                            question_slots["obj2"],
+                            '?'
+                        ] 
+                        # retrieve answers
+                        answer_spans = []
+                        for ans in question_obj['answerJudgments']:
+                            if ans['isValid']: 
+                                answer_spans.extend(ans['spans']) 
+                        answer_spans = list(set(tuple(a) for a in answer_spans))
+                        # answer_spans = list(set(answer_spans))
+                        answer_strs = [' '.join([tokens[i] for i in range(*span)])
+                                    for span in answer_spans]
+                        
+                        yield qa_counter, {
+                            "sentence": sentence,
+                            "sent_id": sent_obj['sentenceId'],
+                            "predicate_idx": predicate_idx,
+                            "predicate": predicate,
+                            "is_verbal": True,
+                            "verb_form": verb_forms['stem'],
+                            "question": question_slots_in_order,
+                            "answers": answer_strs,
+                            "answer_ranges": answer_spans
+                        }
+                        qa_counter += 1
+              
+                
     @classmethod
     def span_from_str(cls, s:str):
         start, end = s.split(":")
         return [int(start), int(end)]
     
-    def _generate_examples(self, qasrl_annotations_paths: List[str], sentences_paths: List[str]):
+    def _generate_examples_from_csv(self, qasrl_annotations_paths: List[str], sentences_paths: List[str]):
 
         """ Yields QASRL examples from a csv file in QASRL-2020/QANom format."""