Upload create_train_from_sharc.py

create_train_from_sharc.py

@@ -0,0 +1,83 @@
+import json
+import argparse
+
+
+def create_entailment_data(train_data):
+    entailment_data = [d for d in train_data if len(d['evidence']) > 0]
+    # entailment data
+    for d in entailment_data:
+        entailment_answer = d['answer'].lower()
+        if d['answer'].lower() not in ['yes', 'no']:
+            entailment_answer = 'maybe'
+        if len(d['history']) > 0:
+            # this means that not all the information needed to get to the answer were provided in the scenario
+            # (they were provided in the history). Therefore the entailment label should be 'maybe'
+            entailment_answer = 'maybe'
+
+        d['entailment_answer'] = entailment_answer
+
+    entailment_path = 'train_entailment_sharc.json'
+    with open(entailment_path, 'w') as f:
+        f.write(json.dumps(entailment_data, indent=True))
+    print('Wrote ShARC entailment data to ' + entailment_path)
+
+    return entailment_data
+
+
+def filter_train_data(sharc_train_path, sharc_dev_path):
+    sharc_train_data = json.load(open(sharc_train_path))
+    sharc_dev_data = json.load(open(sharc_dev_path))
+    sharc_data = sharc_train_data + sharc_dev_data
+
+    train_utterance_ids = open('train_utterance_ids.txt').read().splitlines()
+    train_data = [d for d in sharc_data if d['utterance_id'] in train_utterance_ids]
+    return train_data
+
+
+def create_qa_data(entailment_data):
+    qa_data = []
+    for d in entailment_data:
+        for e in d['evidence']:
+            q_key = 'follow_up_question'
+            a_key = 'follow_up_answer'
+            if 'follow_up_question' in e:
+                qa_data.append({
+                    'utterance_id': d['utterance_id'],
+                    'context': d['scenario'],
+                    'question': e[q_key],
+                    'answer': e[a_key].lower()
+                })
+
+        for h in d['history']:
+            qa_data.append({
+                'utterance_id': d['utterance_id'],
+                'context': d['scenario'],
+                'question': h['follow_up_question'],
+                'answer': 'maybe'
+            })
+        if d['answer'].lower() not in ['yes', 'no']:
+            qa_data.append({
+                'utterance_id': d['utterance_id'],
+                'context': d['scenario'],
+                'question': d['answer'],
+                'answer': 'maybe'
+            })
+    qa_path = 'train_qa_sharc.json'
+    with open(qa_path, 'w') as f:
+        f.write(json.dumps(entailment_data, indent=True))
+    print('Wrote ShARC QA data to ' + qa_path)
+    return qa_data
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser('Script for generating entailment and QA data from ShARC for training')
+    parser.add_argument('-sharc_train_path', type=str, default='sharc_train.json', help='path to ShARC train file')
+    parser.add_argument('-sharc_dev_path', type=str, default='sharc_dev.json', help='path to ShARC dev file')
+
+    args = parser.parse_args()
+    sharc_train_path = args.sharc_train_path
+    sharc_dev_path = args.sharc_dev_path
+
+    train_data = filter_train_data(sharc_train_path, sharc_dev_path)
+    entailment_data = create_entailment_data(train_data)
+    qa_data = create_qa_data(entailment_data)