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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.
The script downloads the data from the original GitHub repository.
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:
{'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).
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