region:us
This dataset contains QA-Alignments - 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.
Here we provide both the QASRL annotations and the QA-Align annotations for the target sentences.
@inproceedings{brook-weiss-etal-2021-qa,
    title = "{QA}-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions",
    author = "Brook Weiss, Daniela  and
      Roit, Paul  and
      Klein, Ayal  and
      Ernst, Ori  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.778",
    pages = "9879--9894",
    abstract = "Multi-text applications, such as multi-document summarization, are typically required to model redundancies across related texts. Current methods confronting consolidation struggle to fuse overlapping information. In order to explicitly represent content overlap, we propose to align predicate-argument relations across texts, providing a potential scaffold for information consolidation. We go beyond clustering coreferring mentions, and instead model overlap with respect to redundancy at a propositional level, rather than merely detecting shared referents. Our setting exploits QA-SRL, utilizing question-answer pairs to capture predicate-argument relations, facilitating laymen annotation of cross-text alignments. We employ crowd-workers for constructing a dataset of QA-based alignments, and present a baseline QA alignment model trained over our dataset. Analyses show that our new task is semantically challenging, capturing content overlap beyond lexical similarity and complements cross-document coreference with proposition-level links, offering potential use for downstream tasks.",
}
# 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).

