init

README.md

@@ -64,11 +64,11 @@ An example of 'train' looks as follows.
 ```
 {
   "question": "What is heresy mainly at odds with?",
-  "passage": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.",
+  "paragraph": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.",
   "answer": "established beliefs or customs",
   "sentence": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs .",
-  "passage_sentence": "<hl> Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs . <hl> A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.",
-  "passage_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl>. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.",
+  "paragraph_sentence": "<hl> Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs . <hl> A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.",
+  "paragraph_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl>. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.",
   "sentence_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl> ."
 }
 ```
@@ -76,16 +76,16 @@ An example of 'train' looks as follows.
 The data fields are the same among all splits.
 #### plain_text
 - `question`: a `string` feature. 
-- `passage`: a `string` feature.
+- `paragraph`: a `string` feature.
 - `answer`: a `string` feature.
 - `sentence`: a `string` feature.
-- `passage_answer`: a `string` feature, which is same as the passage but the answer is highlighted by a special token `<hl>`.
-- `passage_sentence`: a `string` feature, which is same as the passage but a sentence containing the answer is highlighted by a special token `<hl>`.
+- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
+- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
 - `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
 
-Each of `passage_answer`, `passage_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
-but with different information. The `passage_answer` and `sentence_answer` features are for answer-aware question generation and 
-`passage_sentence` feature is for sentence-aware question generation.
+Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
+but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and 
+`paragraph_sentence` feature is for sentence-aware question generation.
 
 ### Data Splits
 

process.py

@@ -32,12 +32,12 @@ def jsonline_reader(filename: str):
 
 def process_single_data(data: Dict):
     """ Convert single raw json data into QG format """
-    example = {'question': data["question"], 'passage': data["context"], 'answer': data["answer"]}
+    example = {'question': data["question"], 'paragraph': data["context"], 'answer': data["answer"]}
 
     # get sentence
-    position = example['passage'].find(example['answer'])
+    position = example['paragraph'].find(example['answer'])
     assert position != -1
-    before_tmp = get_sentence(example['passage'][:position])
+    before_tmp = get_sentence(example['paragraph'][:position])
     if len(before_tmp) == 0:
         before = ''
         before_sentence = ''
@@ -49,7 +49,7 @@ def process_single_data(data: Dict):
             before = ' '.join(before_tmp[:-1])
             before_sentence = before_tmp[-1]
             before_sentence = before_sentence if before_sentence.endswith(' ') else '{} '.format(before_sentence)
-    after_tmp = get_sentence(example['passage'][position + len(example['answer']):])
+    after_tmp = get_sentence(example['paragraph'][position + len(example['answer']):])
     if len(after_tmp) == 0:
         after = ''
         after_sentence = ''
@@ -59,29 +59,27 @@ def process_single_data(data: Dict):
         after_sentence = after_sentence if after_sentence.startswith(' ') else ' {}'.format(after_sentence)
     example['sentence'] = '{}{}{}'.format(before_sentence, example['answer'], after_sentence)
 
-    # get passage_sentence
+    # get paragraph_sentence
     before = '' if before == '' else '{} '.format(before)
     after = '' if after == '' else ' {}'.format(after)
     source_text = '{0}{1} {2} {1}{3}'.format(before, HIGHLIGHT_TOKEN, example['sentence'], after)
-    example['passage_sentence'] = re.sub(r'\s+', ' ', source_text)
+    example['paragraph_sentence'] = re.sub(r'\s+', ' ', source_text)
 
-    # get passage_answer
+    # get paragraph_answer
     source_text = '{0}{1} {2} {1}{3}'.format(
-        example['passage'][:position], HIGHLIGHT_TOKEN, example['answer'],
-        example['passage'][position + len(example['answer']):])
-    example['passage_answer'] = re.sub(r'\s+', ' ', source_text)
+        example['paragraph'][:position], HIGHLIGHT_TOKEN, example['answer'],
+        example['paragraph'][position + len(example['answer']):])
+    example['paragraph_answer'] = re.sub(r'\s+', ' ', source_text)
 
     # get sentence_answer
-    before = get_sentence(example['passage'][:position])
-    if len(before) == 0 or before[-1].endswith('.'):
+    if len(before_tmp) == 0 or before_tmp[-1].endswith('.'):
         before = ''
     else:
-        before = before[-1] if before[-1].endswith(' ') else '{} '.format(before[-1])
-    after = get_sentence(example['passage'][position + len(example['answer']):])
-    if len(after) == 0:
+        before = before_tmp[-1] if before_tmp[-1].endswith(' ') else '{} '.format(before_tmp[-1])
+    if len(after_tmp) == 0:
         after = ''
     else:
-        after = after[0] if after[0].startswith(' ') else ' {}'.format(after[0])
+        after = after_tmp[0] if after_tmp[0].startswith(' ') else ' {}'.format(after_tmp[0])
     source_text = '{0}{1} {2} {1}{3}'.format(before, HIGHLIGHT_TOKEN, example['answer'], after)
     example['sentence_answer'] = re.sub(r'\s+', ' ', source_text)
 

qg_squad.py

@@ -38,16 +38,13 @@ class QGSquad(datasets.GeneratorBasedBuilder):
                     "answer": datasets.Value("string"),
                     "question": datasets.Value("string"),
                     "sentence": datasets.Value("string"),
-                    "passage": datasets.Value("string"),
+                    "paragraph": datasets.Value("string"),
                     "sentence_answer": datasets.Value("string"),
-                    "passage_answer": datasets.Value("string"),
-                    "passage_sentence": datasets.Value("string")
+                    "paragraph_answer": datasets.Value("string"),
+                    "paragraph_sentence": datasets.Value("string")
                 }
             ),
             supervised_keys=None,
-            # task_templates=[
-            #     Summarization(task='question generation', text_column="passage_answer", summary_column='question')
-            # ],
             homepage="https://github.com/asahi417/lm-question-generation"
         )