Datasets:

wrynx
/

probe-robustness-sst2

sentence string	label int64	idx int64	row_id string	orig_split string	orig_idx int64	split string
hide new secretions from the parental units	0	0	sst2-train-0	train	0	train
contains no wit , only labored gags	0	1	sst2-train-1	train	1	train
that loves its characters and communicates something rather beautiful about human nature	1	2	sst2-train-2	train	2	train
on the worst revenge-of-the-nerds clichés the filmmakers could dredge up	0	4	sst2-train-4	train	4	train
demonstrates that the director of such hollywood blockbusters as patriot games can still turn out a small , personal film with an emotional wallop .	1	6	sst2-train-6	train	6	train
of saucy	1	7	sst2-train-7	train	7	train
a depressed fifteen-year-old 's suicidal poetry	0	8	sst2-train-8	train	8	train
goes to absurd lengths	0	10	sst2-train-10	train	10	train
for those moviegoers who complain that ` they do n't make movies like they used to anymore	0	11	sst2-train-11	train	11	train
the part where nothing 's happening ,	0	12	sst2-train-12	train	12	train
saw how bad this movie was	0	13	sst2-train-13	train	13	train
lend some dignity to a dumb story	0	14	sst2-train-14	train	14	train
the greatest musicians	1	15	sst2-train-15	train	15	train
redundant concept	0	18	sst2-train-18	train	18	train
swimming is above all about a young woman 's face , and by casting an actress whose face projects that woman 's doubts and yearnings , it succeeds .	1	19	sst2-train-19	train	19	train
if anything , see it for karen black , who camps up a storm as a fringe feminist conspiracy theorist named dirty dick .	1	21	sst2-train-21	train	21	train
a smile on your face	1	22	sst2-train-22	train	22	train
comes from the brave , uninhibited performances	1	23	sst2-train-23	train	23	train
enriched by an imaginatively mixed cast of antic spirits	1	25	sst2-train-25	train	25	train
which half of dragonfly is worse : the part where nothing 's happening , or the part where something 's happening	0	26	sst2-train-26	train	26	train
in world cinema	1	27	sst2-train-27	train	27	train
the plot is nothing but boilerplate clichés from start to finish ,	0	29	sst2-train-29	train	29	train
the action is stilted	0	30	sst2-train-30	train	30	train
will find little of interest in this film , which is often preachy and poorly acted	0	32	sst2-train-32	train	32	train
sit through ,	0	34	sst2-train-34	train	34	train
more than another `` best man '' clone by weaving a theme throughout this funny film	1	35	sst2-train-35	train	35	train
it 's about issues most adults have to face in marriage and i think that 's what i liked about it -- the real issues tucked between the silly and crude storyline	1	36	sst2-train-36	train	36	train
oblivious to the existence of this film	0	38	sst2-train-38	train	38	train
sharply	1	39	sst2-train-39	train	39	train
the entire point of a shaggy dog story , of course , is that it goes nowhere , and this is classic nowheresville in every sense .	0	40	sst2-train-40	train	40	train
covers this territory with wit and originality , suggesting that with his fourth feature	1	43	sst2-train-43	train	43	train
a $ 40 million version of a game	0	44	sst2-train-44	train	44	train
cross swords with the best of them and	1	46	sst2-train-46	train	46	train
as a fringe feminist conspiracy theorist	0	47	sst2-train-47	train	47	train
proves once again he has n't lost his touch , bringing off a superb performance in an admittedly middling film .	1	48	sst2-train-48	train	48	train
disappointments	0	49	sst2-train-49	train	49	train
a muddle splashed with bloody beauty as vivid as any scorsese has ever given us .	1	51	sst2-train-51	train	51	train
many pointless	0	52	sst2-train-52	train	52	train
a beautifully	1	53	sst2-train-53	train	53	train
poor ben bratt could n't find stardom if mapquest emailed him point-to-point driving directions .	0	56	sst2-train-56	train	56	train
starts with a legend	1	58	sst2-train-58	train	58	train
rich veins of funny stuff in this movie	1	60	sst2-train-60	train	60	train
shot on ugly digital video	0	62	sst2-train-62	train	62	train
... a sour little movie at its core ; an exploration of the emptiness that underlay the relentless gaiety of the 1920 's ... the film 's ending has a `` what was it all for ? ''	0	63	sst2-train-63	train	63	train
though ford and neeson capably hold our interest , but its just not a thrilling movie	0	64	sst2-train-64	train	64	train
is pretty damned funny .	1	65	sst2-train-65	train	65	train
the corporate circus that is the recording industry in the current climate of mergers and downsizing	0	68	sst2-train-68	train	68	train
the storylines are woven together skilfully , the magnificent swooping aerial shots are breathtaking , and the overall experience is awesome .	1	69	sst2-train-69	train	69	train
of the most highly-praised disappointments i	0	70	sst2-train-70	train	70	train
sounds like a cruel deception carried out by men of marginal intelligence , with reactionary ideas about women and a total lack of empathy .	0	71	sst2-train-71	train	71	train
seem fresh	1	72	sst2-train-72	train	72	train
to the dustbin of history	0	73	sst2-train-73	train	73	train
weak and	0	76	sst2-train-76	train	76	train
contains very few laughs and even less surprises	0	78	sst2-train-78	train	78	train
film to affirm love 's power to help people endure almost unimaginable horror	1	79	sst2-train-79	train	79	train
generates	1	81	sst2-train-81	train	81	train
, like life , is n't much fun without the highs and lows	1	82	sst2-train-82	train	82	train
based on a true and historically significant story	1	83	sst2-train-83	train	83	train
well-rounded tribute	1	84	sst2-train-84	train	84	train
, though many of the actors throw off a spark or two when they first appear , they ca n't generate enough heat in this cold vacuum of a comedy to start a reaction .	0	85	sst2-train-85	train	85	train
fashioning an engrossing entertainment out	1	88	sst2-train-88	train	88	train
spiffy animated feature	1	89	sst2-train-89	train	89	train
that 's so sloppily written and cast that you can not believe anyone more central to the creation of bugsy than the caterer	0	90	sst2-train-90	train	90	train
alternating between facetious comic parody and pulp melodrama , this smart-aleck movie ... tosses around some intriguing questions about the difference between human and android life	1	91	sst2-train-91	train	91	train
, generous and subversive artworks	1	93	sst2-train-93	train	93	train
it does n't follow the stale , standard , connect-the-dots storyline which has become commonplace in movies that explore the seamy underbelly of the criminal world	1	94	sst2-train-94	train	94	train
funny yet	1	95	sst2-train-95	train	95	train
overbearing and over-the-top	0	96	sst2-train-96	train	96	train
it 's robert duvall !	1	97	sst2-train-97	train	97	train
rich and sudden wisdom	1	98	sst2-train-98	train	98	train
acted and directed , it 's clear that washington most certainly has a new career ahead of him	1	99	sst2-train-99	train	99	train
in memory	1	100	sst2-train-100	train	100	train
yet this grating showcase	0	102	sst2-train-102	train	102	train
hate to tear your eyes away from the images long enough to read the subtitles	1	103	sst2-train-103	train	103	train
build some robots , haul 'em to the theater with you for the late show , and put on your own mystery science theatre 3000 tribute to what is almost certainly going to go down as the worst -- and only -- killer website movie of this or any other year	0	106	sst2-train-106	train	106	train
do n't work in concert	0	107	sst2-train-107	train	107	train
the direction has a fluid , no-nonsense authority , and the performances by harris , phifer and cam ` ron seal the deal .	1	108	sst2-train-108	train	108	train
would have liked it more if it had just gone that one step further	1	109	sst2-train-109	train	109	train
it 's too harsh to work as a piece of storytelling ,	0	110	sst2-train-110	train	110	train
takes a classic story , casts attractive and talented actors and uses a magnificent landscape to create a feature film that is wickedly fun to watch .	1	112	sst2-train-112	train	112	train
provide its keenest pleasures	1	113	sst2-train-113	train	113	train
altogether too slight to be called any kind of masterpiece	0	114	sst2-train-114	train	114	train
grievous but	0	115	sst2-train-115	train	115	train
after you laugh once ( maybe twice ) , you will have completely forgotten the movie by the time you get back to your car in the parking lot .	0	116	sst2-train-116	train	116	train
hopeless	0	117	sst2-train-117	train	117	train
nicks and steinberg match their own creations for pure venality -- that 's giving it the old college try .	0	119	sst2-train-119	train	119	train
very well-written and very well-acted .	1	120	sst2-train-120	train	120	train
are n't many conclusive answers in the film	0	121	sst2-train-121	train	121	train
clumsy dialogue , heavy-handed phoney-feeling sentiment ,	0	122	sst2-train-122	train	122	train
proves a lovely trifle that , unfortunately , is a little too in love with its own cuteness .	0	123	sst2-train-123	train	123	train
bring tissues .	1	124	sst2-train-124	train	124	train
brings the proper conviction to his role as ( jason bourne ) .	1	126	sst2-train-126	train	126	train
just too silly	0	129	sst2-train-129	train	129	train
cinematic bon bons	1	130	sst2-train-130	train	130	train
it is supremely unfunny and unentertaining to watch middle-age and	0	131	sst2-train-131	train	131	train
a lively and engaging examination of how similar obsessions can dominate a family .	1	132	sst2-train-132	train	132	train
irritates and	0	133	sst2-train-133	train	133	train
collapse	0	134	sst2-train-134	train	134	train
wide-awake all the way through	1	137	sst2-train-137	train	137	train
is one big excuse to play one lewd scene after another .	0	139	sst2-train-139	train	139	train

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Check out the documentation for more information.

sst2

Dataset repo: wrynx/probe-robustness-sst2

Auto-generated by prepare_datasets.py. Do not hand-edit -- regenerate by re-running the script (with --force) instead.

Stats

Total records: 68221
Records per split:
- test: 10910
- train: 46918
- valid: 10393
Number of classes: 2
Records per class:
- 0: 30208
- 1: 38013
Records per class per split:
- test:
  - 0: 4900
  - 1: 6010
- train:
  - 0: 20712
  - 1: 26206
- valid:
  - 0: 4596
  - 1: 5797

Original dataset README (from `nyu-mll/glue`)

Reproduced here from the source dataset's own card (license, citation, task description, etc.) so that information isn't lost by re-hosting under this repo.

Dataset Card for GLUE

Dataset Summary

GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.

Supported Tasks and Leaderboards

The leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:

ax

A manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.

cola

The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.

mnli

The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.

mnli_matched

The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.

mnli_mismatched

The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.

mrpc

The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.

qnli

The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.

qqp

The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.

rte

The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.

sst2

The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.

stsb

The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.

wnli

The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).

Languages

The language data in GLUE is in English (BCP-47 en)

Dataset Structure

Data Instances

ax

Size of downloaded dataset files: 0.22 MB
Size of the generated dataset: 0.24 MB
Total amount of disk used: 0.46 MB

An example of 'test' looks as follows.

{
  "premise": "The cat sat on the mat.",
  "hypothesis": "The cat did not sit on the mat.",
  "label": -1,
  "idx: 0
}

cola

Size of downloaded dataset files: 0.38 MB
Size of the generated dataset: 0.61 MB
Total amount of disk used: 0.99 MB

An example of 'train' looks as follows.

{
  "sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
  "label": 1,
  "id": 0
}

mnli

Size of downloaded dataset files: 312.78 MB
Size of the generated dataset: 82.47 MB
Total amount of disk used: 395.26 MB

An example of 'train' looks as follows.

{
  "premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
  "hypothesis": "Product and geography are what make cream skimming work.",
  "label": 1,
  "idx": 0
}

mnli_matched

Size of downloaded dataset files: 312.78 MB
Size of the generated dataset: 3.69 MB
Total amount of disk used: 316.48 MB

An example of 'test' looks as follows.

{
  "premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.",
  "hypothesis": "Hierbas is a name worth looking out for.",
  "label": -1,
  "idx": 0
}

mnli_mismatched

Size of downloaded dataset files: 312.78 MB
Size of the generated dataset: 3.91 MB
Total amount of disk used: 316.69 MB

An example of 'test' looks as follows.

{
  "premise": "What have you decided, what are you going to do?",
  "hypothesis": "So what's your decision?",
  "label": -1,
  "idx": 0
}

mrpc

Size of downloaded dataset files: ??
Size of the generated dataset: 1.5 MB
Total amount of disk used: ??

An example of 'train' looks as follows.

{
  "sentence1": "Amrozi accused his brother, whom he called "the witness", of deliberately distorting his evidence.",
  "sentence2": "Referring to him as only "the witness", Amrozi accused his brother of deliberately distorting his evidence.",
  "label": 1,
  "idx": 0
}

qnli

Size of downloaded dataset files: ??
Size of the generated dataset: 28 MB
Total amount of disk used: ??

An example of 'train' looks as follows.

{
  "question": "When did the third Digimon series begin?",
  "sentence": "Unlike the two seasons before it and most of the seasons that followed, Digimon Tamers takes a darker and more realistic approach to its story featuring Digimon who do not reincarnate after their deaths and more complex character development in the original Japanese.",
  "label": 1,
  "idx": 0
}

qqp

Size of downloaded dataset files: ??
Size of the generated dataset: 107 MB
Total amount of disk used: ??

An example of 'train' looks as follows.

{
  "question1": "How is the life of a math student? Could you describe your own experiences?",
  "question2": "Which level of prepration is enough for the exam jlpt5?",
  "label": 0,
  "idx": 0
}

rte

Size of downloaded dataset files: ??
Size of the generated dataset: 1.9 MB
Total amount of disk used: ??

An example of 'train' looks as follows.

{
  "sentence1": "No Weapons of Mass Destruction Found in Iraq Yet.",
  "sentence2": "Weapons of Mass Destruction Found in Iraq.",
  "label": 1,
  "idx": 0
}

sst2

Size of downloaded dataset files: ??
Size of the generated dataset: 4.9 MB
Total amount of disk used: ??

An example of 'train' looks as follows.

{
  "sentence": "hide new secretions from the parental units",
  "label": 0,
  "idx": 0
}

stsb

Size of downloaded dataset files: ??
Size of the generated dataset: 1.2 MB
Total amount of disk used: ??

An example of 'train' looks as follows.

{
  "sentence1": "A plane is taking off.",
  "sentence2": "An air plane is taking off.",
  "label": 5.0,
  "idx": 0
}

wnli

Size of downloaded dataset files: ??
Size of the generated dataset: 0.18 MB
Total amount of disk used: ??

An example of 'train' looks as follows.

{
  "sentence1": "I stuck a pin through a carrot. When I pulled the pin out, it had a hole.",
  "sentence2": "The carrot had a hole.",
  "label": 1,
  "idx": 0
}

Data Fields

The data fields are the same among all splits.

ax

premise: a string feature.
hypothesis: a string feature.
label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
idx: a int32 feature.

cola

sentence: a string feature.
label: a classification label, with possible values including unacceptable (0), acceptable (1).
idx: a int32 feature.

mnli

premise: a string feature.
hypothesis: a string feature.
label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
idx: a int32 feature.

mnli_matched

premise: a string feature.
hypothesis: a string feature.
label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
idx: a int32 feature.

mnli_mismatched

premise: a string feature.
hypothesis: a string feature.
label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
idx: a int32 feature.

mrpc

sentence1: a string feature.
sentence2: a string feature.
label: a classification label, with possible values including not_equivalent (0), equivalent (1).
idx: a int32 feature.

qnli

question: a string feature.
sentence: a string feature.
label: a classification label, with possible values including entailment (0), not_entailment (1).
idx: a int32 feature.

qqp

question1: a string feature.
question2: a string feature.
label: a classification label, with possible values including not_duplicate (0), duplicate (1).
idx: a int32 feature.

rte

sentence1: a string feature.
sentence2: a string feature.
label: a classification label, with possible values including entailment (0), not_entailment (1).
idx: a int32 feature.

sst2

sentence: a string feature.
label: a classification label, with possible values including negative (0), positive (1).
idx: a int32 feature.

stsb

sentence1: a string feature.
sentence2: a string feature.
label: a float32 regression label, with possible values from 0 to 5.
idx: a int32 feature.

wnli

sentence1: a string feature.
sentence2: a string feature.
label: a classification label, with possible values including not_entailment (0), entailment (1).
idx: a int32 feature.

Data Splits

ax

	test
ax	1104

cola

	train	validation	test
cola	8551	1043	1063

mnli

	train	validation_matched	validation_mismatched	test_matched	test_mismatched
mnli	392702	9815	9832	9796	9847

mnli_matched

	validation	test
mnli_matched	9815	9796

mnli_mismatched

	validation	test
mnli_mismatched	9832	9847

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

The primary GLUE tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset.

Citation Information

If you use GLUE, please cite all the datasets you use.

In addition, we encourage you to use the following BibTeX citation for GLUE itself:

@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}

If you evaluate using GLUE, we also highly recommend citing the papers that originally introduced the nine GLUE tasks, both to give the original authors their due credit and because venues will expect papers to describe the data they evaluate on. The following provides BibTeX for all of the GLUE tasks, except QQP, for which we recommend adding a footnote to this page: https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs

@article{warstadt2018neural,
  title={Neural Network Acceptability Judgments},
  author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R.},
  journal={arXiv preprint 1805.12471},
  year={2018}
}
@inproceedings{socher2013recursive,
  title={Recursive deep models for semantic compositionality over a sentiment treebank},
  author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
  booktitle={Proceedings of EMNLP},
  pages={1631--1642},
  year={2013}
}
@inproceedings{dolan2005automatically,
  title={Automatically constructing a corpus of sentential paraphrases},
  author={Dolan, William B and Brockett, Chris},
  booktitle={Proceedings of the International Workshop on Paraphrasing},
  year={2005}
}
@book{agirre2007semantic,
  editor    = {Agirre, Eneko and M`arquez, Llu'{i}s and Wicentowski, Richard},
  title     = {Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)},
  month     = {June},
  year      = {2007},
  address   = {Prague, Czech Republic},
  publisher = {Association for Computational Linguistics},
}
@inproceedings{williams2018broad,
  author    = {Williams, Adina and Nangia, Nikita and Bowman, Samuel R.},
  title = {A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference},
  booktitle = {Proceedings of NAACL-HLT},
  year = 2018
}
@inproceedings{rajpurkar2016squad,
  author = {Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy}
  title = {{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text},
  booktitle = {Proceedings of EMNLP}
  year = {2016},
  publisher = {Association for Computational Linguistics},
  pages = {2383--2392},
  location = {Austin, Texas},
}
@incollection{dagan2006pascal,
  title={The {PASCAL} recognising textual entailment challenge},
  author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},
  booktitle={Machine learning challenges. evaluating predictive uncertainty, visual object classification, and recognising tectual entailment},
  pages={177--190},
  year={2006},
  publisher={Springer}
}
@article{bar2006second,
  title={The second {PASCAL} recognising textual entailment challenge},
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  year={2006}
}
@inproceedings{giampiccolo2007third,
  title={The third {PASCAL} recognizing textual entailment challenge},
  author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},
  booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},
  pages={1--9},
  year={2007},
  organization={Association for Computational Linguistics},
}
@article{bentivogli2009fifth,
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  booktitle={TAC},
  year={2009}
}
@inproceedings{levesque2011winograd,
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  author={Levesque, Hector J and Davis, Ernest and Morgenstern, Leora},
  booktitle={{AAAI} Spring Symposium: Logical Formalizations of Commonsense Reasoning},
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  pages={47},
  year={2011}
}