| # 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). | |