Datasets:

dinho1597
/

qasper-rag

query_id stringlengths 40 40	answer stringlengths 1 1.63k
753990d0b621d390ed58f20c4d9e4f065f0dc672	a vocabulary of positive and negative predicates that helps determine the polarity score of an event
9d578ddccc27dd849244d632dd0f6bf27348ad81	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 -...
02e4bf719b1a504e385c35c6186742e720bcb281	cause relation: both events in the relation should have the same polarity; concession relation: events should have opposite polarity
44c4bd6decc86f1091b5fc0728873d9324cdde4e	7000000 pairs of events were extracted from the Japanese Web corpus, 529850 pairs of events were extracted from the ACP corpus
86abeff85f3db79cf87a8c993e5e5aa61226dc98	negative positive
39f8db10d949c6b477fa4b51e7c184016505884f	by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity
d0bc782961567dc1dd7e074b621a6d6be44bb5b4	30 words
a592498ba2fac994cd6fad7372836f0adb37e22a	100 million sentences
3a9d391d25cde8af3334ac62d478b36b30079d74	Yes
8d8300d88283c73424c8f301ad9fdd733845eb47	confusion matrices of labels between annotators
48b12eb53e2d507343f19b8a667696a39b719807	feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking) Emotions that exhibit this dual capacity have been defined as “aesthetic emotions”
003f884d3893532f8c302431c9f70be6f64d9be8	No
bb97537a0a7c8f12a3f65eba73cefa6abcd2f2b2	Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average number o...
eea089baedc0ce80731c8fdcb064b82f584f483a	communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members within distinctive communities, established users have an increased propensity to engage with the community's speciali...
edb2d24d6d10af13931b3a47a6543bd469752f0c	They collect subreddits from January 2013 to December 2014,2 for which there are at least 500 words in the vocabulary used to estimate the measures, in at least 4 months of the subreddit’s history. They compute our measures over the comments written by users in a community in time windows of months, for each sufficient...
938cf30c4f1d14fa182e82919e16072fdbcf2a82	the average volatility of all utterances
93f4ad6568207c9bd10d712a52f8de25b3ebadd4	the average specificity of all utterances
71a7153e12879defa186bfb6dbafe79c74265e10	Chinese general corpus
85d1831c28d3c19c84472589a252e28e9884500f	QANet BIBREF39 BERT-Base BIBREF26
1959e0ebc21fafdf1dd20c6ea054161ba7446f61	Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the spe...
77cf4379106463b6ebcb5eb8fa5bb25450fa5fb8	three types of questions, namely tumor size, proximal resection margin and distal resection margin
06095a4dee77e9a570837b35fc38e77228664f91	the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences
19c9cfbc4f29104200393e848b7b9be41913a7ac	2,714
08a5f8d36298b57f6a4fcb4b6ae5796dc5d944a4	integrate clinical named entity information into pre-trained language model
975a4ac9773a4af551142c324b64a0858670d06e	17,833 sentences, 826,987 characters and 2,714 question-answer pairs
326e08a0f5753b90622902bd4a9c94849a24b773	17,833 sentences, 826,987 characters and 2,714 question-answer pairs
bd78483a746fda4805a7678286f82d9621bc45cf	state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26
dd155f01f6f4a14f9d25afc97504aefdc6d29c13	Quality measures using perplexity and recall, and performance measured using latency and energy usage.
a9d530d68fb45b52d9bad9da2cd139db5a4b2f7c	Kneser–Ney smoothing
e07df8f613dbd567a35318cd6f6f4cb959f5c82d	perplexity
1a43df221a567869964ad3b275de30af2ac35598	Yelp Challenge dataset BIBREF2
12f1919a3e8ca460b931c6cacc268a926399dff4	AdaBoost-based classifier
cd1034c183edf630018f47ff70b48d74d2bb1649	Yes
bd9930a613dd36646e2fc016b6eb21ab34c77621	1,006 fake reviews and 994 real reviews
6e2ad9ad88cceabb6977222f5e090ece36aa84ea	The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We tra...
aacb0b97aed6fc6a8b471b8c2e5c4ddb60988bf5	one
710c1f8d4c137c8dad9972f5ceacdbf8004db208	No
47726be8641e1b864f17f85db9644ce676861576	We compare this method of bias mitigation with the no bias mitigation ("Orig"), geometric bias mitigation ("Geo"), the two pieces of our method alone ("Prob" and "KNN") and the composite method ("KNN+Prob"). We note that the composite method performs reasonably well according the the RIPA metric, and much better than t...
8958465d1eaf81c8b781ba4d764a4f5329f026aa	RIPA, Neighborhood Metric, WEAT
347e86893e8002024c2d10f618ca98e14689675f	only high-quality data helps
cbf1137912a47262314c94d36ced3232d5fa1926	fastText CWE-LP
99a10823623f78dbff9ccecb210f187105a196e9	large Portuguese corpus
09f0dce416a1e40cc6a24a8b42a802747d2c9363	Continuous Bag-of-Words (CBOW)
ac706631f2b3fa39bf173cd62480072601e44f66	Yes
8b71ede8170162883f785040e8628a97fc6b5bcb	it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher ...
fa2a384a23f5d0fe114ef6a39dced139bddac20e	903019 references
bdd8368debcb1bdad14c454aaf96695ac5186b09	Given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively, the estimated intensity is established as mean squared error.
3334f50fe1796ce0df9dd58540e9c08be5856c23	For each user, we calculate the proportion of tweets scored positively by each LIWC category.
7081b6909cb87b58a7b85017a2278275be58bf60	210
1870f871a5bcea418c44f81f352897a2f53d0971	DOSPERT, BSSS and VIAS
ce6201435cc1196ad72b742db92abd709e0f9e8d	Yes
928828544e38fe26c53d81d1b9c70a9fb1cc3feb	29,500 documents in the CORD-19 corpus (2020-03-13)
8fc14714eb83817341ada708b9a0b6b4c6ab5023	manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15
d94ac550dfdb9e4bbe04392156065c072b9d75e1	Yes
eeb6e0caa4cf5fdd887e1930e22c816b99306473	The contexts are manually labelled with WordNet senses of the target words
3c0eaa2e24c1442d988814318de5f25729696ef5	Yes
922f1b740f8b13fdc8371e2a275269a44c86195e	Yes
b39f2249a1489a2cef74155496511cc5d1b2a73d	Answer with content missing: (Table 1) Previous state-of-the art on same dataset: ResNet50 89% (6 languages), SVM-HMM 70% (4 languages)
591231d75ff492160958f8aa1e6bfcbbcd85a776	CNN-mean CNN-avgmax
9e805020132d950b54531b1a2620f61552f06114	CNN-mean CNN-avgmax
95abda842c4df95b4c5e84ac7d04942f1250b571	multiple language pairs including German-English, French-English, and Japanese-English.
2419b38624201d678c530eba877c0c016cccd49f	Yes
b99d100d17e2a121c3c8ff789971ce66d1d40a4d	we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models)
578d0b23cb983b445b1a256a34f969b34d332075	Arap-Tweet BIBREF19 an in-house Twitter dataset for gender the MADAR shared task 2 BIBREF20 the LAMA-DINA dataset from BIBREF22 LAMA-DIST Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24 BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34
6548db45fc28e8a8b51f114635bad14a13eaec5b	We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 .
4c4f76837d1329835df88b0921f4fe8bda26606f	No
819d2e97f54afcc7cdb3d894a072bcadfba9b747	CNN, TIME, 20 Newsgroups, and Reuters-21578
637aa32a34b20b4b0f1b5dfa08ef4e0e5ed33d52	Yes
4b8257cdd9a60087fa901da1f4250e7d910896df	typos in spellings or ungrammatical words
abc5836c54fc2ac8465aee5a83b9c0f86c6fd6f5	No
4debd7926941f1a02266b1a7be2df8ba6e79311a	No
3b745f086fb5849e7ce7ce2c02ccbde7cfdedda5	In the sentiment classification task by 6% to 8% and in the intent classification task by 0.94% on average
44c7c1fbac80eaea736622913d65fe6453d72828	34,432 user conversations
3e0c9469821cb01a75e1818f2acb668d071fcf40	overall rating mean number of turns
a725246bac4625e6fe99ea236a96ccb21b5f30c6	Amazon Conversational Bot Toolkit natural language understanding (NLU) (nlu) module dialog manager knowledge bases natural language generation (NLG) (nlg) module text to speech (TTS) (tts)
516626825e51ca1e8a3e0ac896c538c9d8a747c8	No
77af93200138f46bb178c02f710944a01ed86481	Yes
71538776757a32eee930d297f6667cd0ec2e9231	modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions
680dc3e56d1dc4af46512284b9996a1056f89ded	FastText BERT two-layer BiLSTM architecture with GloVe word embeddings
bd5379047c2cf090bea838c67b6ed44773bcd56f	They used BERT-based models to detect subjective language in the WNC corpus
7aa8375cdf4690fc3b9b1799b0f5a9ec1c1736ed	No, other baseline metrics they use besides ROUGE-L are n-gram overlap, negative cross-entropy, perplexity, and BLEU.
3ac30bd7476d759ea5d9a5abf696d4dfc480175b	LSTM LMs
f0317e48dafe117829e88e54ed2edab24b86edb1	if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations
f129c97a81d81d32633c94111018880a7ffe16d1	Soft attention Hard Stochastic attention Local Attention
100cf8b72d46da39fedfe77ec939fb44f25de77f	dataset that contains article-comment parallel contents INLINEFORM0 , and an unpaired dataset that contains the documents (articles or comments) INLINEFORM1
8cc56fc44136498471754186cfa04056017b4e54	Under the retrieval evaluation setting, their proposed model + IR2 had better MRR than NVDM by 0.3769, better MR by 4.6, and better Recall@10 by 20 . Under the generative evaluation setting the proposed model + IR2 had better BLEU by 0.044 , better CIDEr by 0.033, better ROUGE by 0.032, and better METEOR by 0.029
5fa431b14732b3c47ab6eec373f51f2bca04f614	TF-IDF NVDM
33ccbc401b224a48fba4b167e86019ffad1787fb	from 50K to 4.8M
cca74448ab0c518edd5fc53454affd67ac1a201c	198,112
b69ffec1c607bfe5aa4d39254e0770a3433a191b	Chinese dataset BIBREF0
f5cf8738e8d211095bb89350ed05ee7f9997eb19	up to four percentage points in accuracy
bed527bcb0dd5424e69563fba4ae7e6ea1fca26a	2019 GermEval shared task on hierarchical text classification
aeab5797b541850e692f11e79167928db80de1ea	all three representations are concatenated and passed into a MLP
bfa3776c30cb30e0088e185a5908e5172df79236	Random Forest Ensemble classifiers
a2a66726a5dca53af58aafd8494c4de833a06f14	Yes
ee87608419e4807b9b566681631a8cd72197a71a	The Digital Library in the TextGrid Repository
cda4612b4bda3538d19f4b43dde7bc30c1eda4e5	automated attribute-value extraction score the attributes using the Bayes model evaluate their importance with several different frequency metrics aggregate the weights from different sources into one consistent typicality score using a Ranking SVM model OntoRank algorithm
e12674f0466f8c0da109b6076d9939b30952c7da	FastText
b5c3787ab3784214fc35f230ac4926fe184d86ba	Yes
9174aded45bc36915f2e2adb6f352f3c7d9ada8b	SST-2 (Stanford Sentiment Treebank, version 2) Snips

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QASPER RAG

Dataset for Retrieval-Augmented Generation (RAG) based on QASPER.

Structure

Subset	Splits	Description
`corpus`	train (default)	Paper chunks (abstract + full-text paragraphs) shared across all query splits
`queries`	train, dev, test	Information-seeking questions over scientific papers
`qrels`	train, dev, test	Relevance judgments (query ↔ paragraph chunk)
`answers`	train, dev, test	Reference answers (longest valid free-form answer)

Dataset statistics

Split	Queries	Corpus
train	2101	81550
dev	890	81550
test	1310	81550

The corpus is shared across all splits and contains paragraph-level chunks from the abstract and full text of each paper.

Dev split: mapped from the original validation split
Corpus source: unique papers from train, validation and test splits
Chunking: one chunk for the abstract (section_name: abstract) and one chunk per paragraph in full_text

Source

Component	QASPER resource
Train	`train` split from allenai/qasper
Dev	`validation` split
Test	`test` split
Corpus	`abstract` + `full_text.paragraphs`
Queries	`qas.question`
Qrels	`qas.answers[].answer[].evidence` matched to corpus chunks
Answers	Longest valid answer per question (`free_form_answer`, joined `extractive_spans`, or `Yes`/`No`)

Filtering

Questions are kept only when at least one answer satisfies all of the following:

unanswerable is false
the answer has free_form_answer, non-empty extractive_spans, or yes_no
after removing evidence items containing FLOAT SELECTED, at least one answer still has evidence that matches the corpus

Evidence items containing FLOAT SELECTED are removed individually. Questions are omitted only when no valid answer has remaining evidence.

Schema

corpus

{"id": "...", "title": "...", "section_name": "...", "text": "..."}

queries

{"id": "...", "text": "..."}

qrels

{"query_id": "...", "corpus_id": "...", "score": 1}

answers

{"query_id": "...", "answer": "..."}

Usage

from datasets import load_dataset

corpus = load_dataset("dinho1597/qasper-rag", "corpus")["train"]
queries = load_dataset("dinho1597/qasper-rag", "queries")
qrels = load_dataset("dinho1597/qasper-rag", "qrels")
answers = load_dataset("dinho1597/qasper-rag", "answers")

train_queries = queries["train"]
dev_qrels = qrels["dev"]
test_answers = answers["test"]

Citation

QASPER is released under the CC BY 4.0 License.

@inproceedings{Dasigi2021ADO,
  title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
  author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
  year={2021}
}

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