Datasets:

Ahren09
/

SARA-QASPER

id stringlengths 1 4	example_id stringlengths 1 3	question stringlengths 12 167	context listlengths 1 168	answer stringlengths 0 1.61k	choices null	question_type stringclasses 3 values
0	0	What is the seed lexicon?	[ "Experiments ::: Results and Discussion\t Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in ...	a vocabulary of positive and negative predicates that helps determine the polarity score of an event	null	free_form
1	0	What are the results?	[ "This suggests that the training set of 0.6 million events is sufficiently large for training the models. For comparison, we trained the models with a subset (6,000 events) of the ACP dataset. As the results shown in Table TABREF24 demonstrate, our method is effective when labeled data are small. The result of hype...	Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA+CO -...	null	free_form
2	0	How are relations used to propagate polarity?	[ "Related Work\tLearning affective events is closely related to sentiment analysis. Whereas sentiment analysis usually focuses on the polarity of what are described (e.g., movies), we work on how people are typically affected by events. In sentiment analysis, much attention has been paid to compositionality. Word-le...	based on the relation between events, the suggested polarity of one event can determine the possible polarity of the other event	null	free_form
3	0	How big is the Japanese data?	[ "Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network mod...	7000000 pairs of events were extracted from the Japanese Web corpus, 529850 pairs of events were extracted from the ACP corpus	null	free_form
4	0	What are labels available in dataset for supervision?	[ "Experiments ::: Results and Discussion\t Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in ...	negative, positive	null	extractive
5	0	How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach?	[ "Minimally Supervised Learning of Affective Events Using Discourse Relations\tRecognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable...	3%	null	free_form
6	0	How does their model learn using mostly raw data?	[ "Minimally Supervised Learning of Affective Events Using Discourse Relations\tRecognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable...	by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity	null	free_form
7	0	How big is seed lexicon used for training?	[ "Experiments ::: Results and Discussion\t Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in ...	30 words	null	free_form
8	0	How large is raw corpus used for training?	[ "Compared with this method, our discourse relation-based linking of events is much simpler and more intuitive. Some previous studies made use of document structure to understand the sentiment. proposed a sentiment-specific pre-training strategy using unlabeled dialog data (tweet-reply pairs). proposed a method of...	100 million sentences	null	extractive
9	1	Does the paper report macro F1?	["The best model overall is DBMDZ (.520), showing a balanced response on both validation and test se(...TRUNCATED)	Yes	null	yes_no

End of preview. Expand in Data Studio

SARA QASPER (reformatted)

Reformatted QASPER data used by SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression (ACL 2026, arXiv:2507.05633). Code: Ahren09/SARA.

The SARA Quick Start (python -m src.data.make_qasper_splits) downloads this dataset automatically; you can also load it directly:

from datasets import load_dataset

qa = load_dataset("Ahren09/SARA-QASPER", "qa")                          # train / test
align = load_dataset("Ahren09/SARA-QASPER", "compression_alignment")    # train / validation

Configs

`qa` (default)

One record per answerable QASPER question. train (2,321 rows over the official train papers) and test (1,312 rows, official test papers).

Field	Type	Description
`id`	str	Running row index.
`example_id`	str	Paper index — used for leakage-safe document-level train/dev splitting.
`question`	str	The QASPER question.
`context`	list[str]	BM25-ranked paper contexts, each formatted `"Section name\t<text>"`. Retrieval in SARA is built in-memory from this field; no separate index is needed.
`answer`	str	Short gold answer (a span, value, phrase, or yes/no) derived from the official QASPER annotations. Used as both the training target and the evaluation reference.
`choices`	null	Unused for QASPER (kept for schema compatibility with multiple-choice datasets).
`question_type`	str	One of `extractive`, `free_form`, `yes_no`, `unanswerable`.

Models trained on this data answer with chain-of-thought followed by the final short answer wrapped in tags: ... reasoning ... <answer>short answer</answer>; evaluation extracts the tagged span and scores it against answer.

`compression_alignment`

Text snippets from QASPER paper bodies used for the SARA projector-alignment warm-up (the projector learns to reconstruct a document from its semantic compression vector before QA fine-tuning). train (22,111 rows) and validation (200 rows); each record is {"text": "<document text snippet>"}.

Provenance

Derived from allenai/qasper (Dasigi et al., NAACL 2021), released under CC BY 4.0. This derivative is released under the same license with attribution.
context was built from each paper's title/abstract/sections and BM25-ranked per question; BIBREF citation markers were stripped.
Earlier revisions of this dataset carried an additional LLM-rewritten answer_reformatted field; it has been removed — the short gold answer is the single reference. The old revision remains available in this repo's git history.

Checksums (sha256)

0ec3d1bbab2f85341a9432b263ca90669f5cfd45bb2163170cedddf8ff60742c  QASPER_train.jsonl
a26afe8a11e350e5a860c83b75ee4938b2b89f19dc4883cea063dd90642bc1e1  QASPER_test.jsonl
1d6386a84408127a20f01db92f8b9a73ec9499d884d0cab3c2c9f07c4494aa12  QASPER_compression_alignment_train.jsonl
9de81c3cbf42aa4b4001a57762c37f981eb2ae0c5b468c3bfebffe65438f8a0f  QASPER_compression_alignment_dev.jsonl

Citation

@inproceedings{jin2025sara,
  title={SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression},
  author={Jin, Yiqiao and Sharma, Kartik and Rakesh, Vineeth and Dou, Yingtong and Pan, Menghai and Das, Mahashweta and Kumar, Srijan},
  booktitle={ACL},
  year={2026}
}

@inproceedings{dasigi2021dataset,
  title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
  author={Dasigi, Pradeep and Lo, Kyle and Beltagy, Iz and Cohan, Arman and Smith, Noah A. and Gardner, Matt},
  booktitle={NAACL},
  year={2021}
}