Aligning "no-QA sentences" denotation to QANom - records for sentences with no QAs would have both 'question' and 'answers' as empty lists

qa_discourse.py

@@ -61,7 +61,7 @@ COLUMNS = ['qasrl_id', 'sentence', 'worker_id', 'full_question', 'full_answer',
 class QaDiscourse(datasets.GeneratorBasedBuilder):
     """QA-Discourse: Discourse Relations as Question-Answer Pairs.  """
 
-    VERSION = datasets.Version("1.0.1")
+    VERSION = datasets.Version("1.0.2")
 
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
@@ -135,7 +135,10 @@ class QaDiscourse(datasets.GeneratorBasedBuilder):
     
     def _generate_examples(self, filepaths: List[str]):
 
-        """ Yields QA-Discourse examples from a tsv file."""
+        """ 
+        Yields QA-Discourse examples from a tsv file.
+        Sentences with no QAs will yield an ``empty QA'' record, where both 'question' and 'answers' are empty lists.
+        """
 
         # merge annotations from sections 
         df = pd.concat([pd.read_csv(fn, sep='\t', error_bad_lines=False) for fn in filepaths]).reset_index(drop=True) 
@@ -143,10 +146,14 @@ class QaDiscourse(datasets.GeneratorBasedBuilder):
         for counter, row in df.iterrows():
             # Prepare question (3 "slots" and question mark)
             question = [row.question_start, row.question_aux, row.question_body.rstrip('?'), '?']
+            answer = [row.answer]
+            if row.question_start == "_":    # sentence has no QAs
+                question = []
+                answer = []
             
             yield counter, {
                 "sentence": row.sentence,
                 "sent_id": row.qasrl_id,
                 "question": question,
-                "answers": [row.answer],
+                "answers": answer,
             }