Create README.md

README.md

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+# QA-Align
+
+This dataset contains QA-Alignments --- fine-grained annotations of cross-text content overlap. 
+The task input is two sentences from two documents, roughly talking about the same event, along with their QA-SRL annotations 
+which capture verbal predicate-argument relations in question-answer format. The output is a cross-sentence alignment between sets of QAs which denote the same information.   
+
+See the paper for details: [QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions, Brook Weiss et. al., EMNLP 2021](https://aclanthology.org/2021.emnlp-main.778/).
+
+The script downloads the data from the original [GitHub repository](https://github.com/DanielaBWeiss/QA-ALIGN). 
+
+### Format
+
+The dataset contains the following important features:
+ 
+* `abs_sent_id_1`, `abs_sent_id_2` - unique sentence ids, unique across all data sources. 
+* `text_1`, `text_2`, `prev_text_1`, `prev_text_2` - the two candidate sentences for alignments. The "prev" (previous) sentences are for context (shown to workers and for the model). 
+* `qas_1`, `qas_2` - the sets of QASRL QAs for each sentence. For test and dev they were created by workers, while in train, the QASRL parser generated them. 
+* `alignments` - the aligned QAs that workers have matched. This is the list of qa-alignments, where a single alignment looks like this:
+
+```json
+{'sent1': [{'qa_uuid': '33_1ecbplus~!~8~!~195~!~12~!~charged~!~4082',
+    'verb': 'charged',
+    'verb_idx': 12,
+    'question': 'Who was charged?',
+    'answer': 'the two youths',
+    'answer_range': '9:11'}],
+  'sent2': [{'qa_uuid': '33_8ecbplus~!~3~!~328~!~11~!~accused~!~4876',
+    'verb': 'accused',
+    'verb_idx': 11,
+    'question': 'Who was accused of something?',
+    'answer': 'two men',
+    'answer_range': '9:10'}]}
+```
+Where the for each sentence, we save a list of the aligned QAs from that sentence. 
+
+Note that this single alignment may contain multiple QAs for each sentence. While 96% of the data are one-to-one alignments, 4% contain many-to-many alignment (although most of the time it's a 2-to-1).
+