Datasets:
imppres

	---
	annotations_creators:
	- machine-generated
	language_creators:
	- machine-generated
	language:
	- en
	license:
	- cc-by-nc-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 1K<n<10K
	source_datasets:
	- original
	task_categories:
	- text-classification
	task_ids:
	- natural-language-inference
	paperswithcode_id: imppres
	pretty_name: IMPPRES
	dataset_info:
	- config_name: implicature_connectives
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: gold_label_log
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: gold_label_prag
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: spec_relation
	dtype: string
	- name: item_type
	dtype: string
	- name: trigger
	dtype: string
	- name: lexemes
	dtype: string
	splits:
	- name: connectives
	num_bytes: 221844
	num_examples: 1200
	download_size: 25478
	dataset_size: 221844
	- config_name: implicature_gradable_adjective
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: gold_label_log
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: gold_label_prag
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: spec_relation
	dtype: string
	- name: item_type
	dtype: string
	- name: trigger
	dtype: string
	- name: lexemes
	dtype: string
	splits:
	- name: gradable_adjective
	num_bytes: 153648
	num_examples: 1200
	download_size: 17337
	dataset_size: 153648
	- config_name: implicature_gradable_verb
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: gold_label_log
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: gold_label_prag
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: spec_relation
	dtype: string
	- name: item_type
	dtype: string
	- name: trigger
	dtype: string
	- name: lexemes
	dtype: string
	splits:
	- name: gradable_verb
	num_bytes: 180678
	num_examples: 1200
	download_size: 21504
	dataset_size: 180678
	- config_name: implicature_modals
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: gold_label_log
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: gold_label_prag
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: spec_relation
	dtype: string
	- name: item_type
	dtype: string
	- name: trigger
	dtype: string
	- name: lexemes
	dtype: string
	splits:
	- name: modals
	num_bytes: 178536
	num_examples: 1200
	download_size: 21179
	dataset_size: 178536
	- config_name: implicature_numerals_10_100
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: gold_label_log
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: gold_label_prag
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: spec_relation
	dtype: string
	- name: item_type
	dtype: string
	- name: trigger
	dtype: string
	- name: lexemes
	dtype: string
	splits:
	- name: numerals_10_100
	num_bytes: 208596
	num_examples: 1200
	download_size: 22640
	dataset_size: 208596
	- config_name: implicature_numerals_2_3
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: gold_label_log
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: gold_label_prag
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: spec_relation
	dtype: string
	- name: item_type
	dtype: string
	- name: trigger
	dtype: string
	- name: lexemes
	dtype: string
	splits:
	- name: numerals_2_3
	num_bytes: 188760
	num_examples: 1200
	download_size: 22218
	dataset_size: 188760
	- config_name: implicature_quantifiers
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: gold_label_log
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: gold_label_prag
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: spec_relation
	dtype: string
	- name: item_type
	dtype: string
	- name: trigger
	dtype: string
	- name: lexemes
	dtype: string
	splits:
	- name: quantifiers
	num_bytes: 176790
	num_examples: 1200
	download_size: 21017
	dataset_size: 176790
	- config_name: presupposition_all_n_presupposition
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: trigger
	dtype: string
	- name: trigger1
	dtype: string
	- name: trigger2
	dtype: string
	- name: presupposition
	dtype: string
	- name: gold_label
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: UID
	dtype: string
	- name: pairID
	dtype: string
	- name: paradigmID
	dtype: int16
	splits:
	- name: all_n_presupposition
	num_bytes: 458460
	num_examples: 1900
	download_size: 43038
	dataset_size: 458460
	- config_name: presupposition_both_presupposition
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: trigger
	dtype: string
	- name: trigger1
	dtype: string
	- name: trigger2
	dtype: string
	- name: presupposition
	dtype: string
	- name: gold_label
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: UID
	dtype: string
	- name: pairID
	dtype: string
	- name: paradigmID
	dtype: int16
	splits:
	- name: both_presupposition
	num_bytes: 432760
	num_examples: 1900
	download_size: 41142
	dataset_size: 432760
	- config_name: presupposition_change_of_state
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: trigger
	dtype: string
	- name: trigger1
	dtype: string
	- name: trigger2
	dtype: string
	- name: presupposition
	dtype: string
	- name: gold_label
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: UID
	dtype: string
	- name: pairID
	dtype: string
	- name: paradigmID
	dtype: int16
	splits:
	- name: change_of_state
	num_bytes: 308595
	num_examples: 1900
	download_size: 35814
	dataset_size: 308595
	- config_name: presupposition_cleft_existence
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: trigger
	dtype: string
	- name: trigger1
	dtype: string
	- name: trigger2
	dtype: string
	- name: presupposition
	dtype: string
	- name: gold_label
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: UID
	dtype: string
	- name: pairID
	dtype: string
	- name: paradigmID
	dtype: int16
	splits:
	- name: cleft_existence
	num_bytes: 363206
	num_examples: 1900
	download_size: 37597
	dataset_size: 363206
	- config_name: presupposition_cleft_uniqueness
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: trigger
	dtype: string
	- name: trigger1
	dtype: string
	- name: trigger2
	dtype: string
	- name: presupposition
	dtype: string
	- name: gold_label
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: UID
	dtype: string
	- name: pairID
	dtype: string
	- name: paradigmID
	dtype: int16
	splits:
	- name: cleft_uniqueness
	num_bytes: 388747
	num_examples: 1900
	download_size: 38279
	dataset_size: 388747
	- config_name: presupposition_only_presupposition
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: trigger
	dtype: string
	- name: trigger1
	dtype: string
	- name: trigger2
	dtype: string
	- name: presupposition
	dtype: string
	- name: gold_label
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: UID
	dtype: string
	- name: pairID
	dtype: string
	- name: paradigmID
	dtype: int16
	splits:
	- name: only_presupposition
	num_bytes: 348986
	num_examples: 1900
	download_size: 38126
	dataset_size: 348986
	- config_name: presupposition_possessed_definites_existence
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: trigger
	dtype: string
	- name: trigger1
	dtype: string
	- name: trigger2
	dtype: string
	- name: presupposition
	dtype: string
	- name: gold_label
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: UID
	dtype: string
	- name: pairID
	dtype: string
	- name: paradigmID
	dtype: int16
	splits:
	- name: possessed_definites_existence
	num_bytes: 362302
	num_examples: 1900
	download_size: 38712
	dataset_size: 362302
	- config_name: presupposition_possessed_definites_uniqueness
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: trigger
	dtype: string
	- name: trigger1
	dtype: string
	- name: trigger2
	dtype: string
	- name: presupposition
	dtype: string
	- name: gold_label
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: UID
	dtype: string
	- name: pairID
	dtype: string
	- name: paradigmID
	dtype: int16
	splits:
	- name: possessed_definites_uniqueness
	num_bytes: 459371
	num_examples: 1900
	download_size: 42068
	dataset_size: 459371
	- config_name: presupposition_question_presupposition
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: trigger
	dtype: string
	- name: trigger1
	dtype: string
	- name: trigger2
	dtype: string
	- name: presupposition
	dtype: string
	- name: gold_label
	dtype:
	class_label:
	names:
	'0': entailment
	'1': neutral
	'2': contradiction
	- name: UID
	dtype: string
	- name: pairID
	dtype: string
	- name: paradigmID
	dtype: int16
	splits:
	- name: question_presupposition
	num_bytes: 397195
	num_examples: 1900
	download_size: 41247
	dataset_size: 397195
	configs:
	- config_name: implicature_connectives
	data_files:
	- split: connectives
	path: implicature_connectives/connectives-*
	- config_name: implicature_gradable_adjective
	data_files:
	- split: gradable_adjective
	path: implicature_gradable_adjective/gradable_adjective-*
	- config_name: implicature_gradable_verb
	data_files:
	- split: gradable_verb
	path: implicature_gradable_verb/gradable_verb-*
	- config_name: implicature_modals
	data_files:
	- split: modals
	path: implicature_modals/modals-*
	- config_name: implicature_numerals_10_100
	data_files:
	- split: numerals_10_100
	path: implicature_numerals_10_100/numerals_10_100-*
	- config_name: implicature_numerals_2_3
	data_files:
	- split: numerals_2_3
	path: implicature_numerals_2_3/numerals_2_3-*
	- config_name: implicature_quantifiers
	data_files:
	- split: quantifiers
	path: implicature_quantifiers/quantifiers-*
	- config_name: presupposition_all_n_presupposition
	data_files:
	- split: all_n_presupposition
	path: presupposition_all_n_presupposition/all_n_presupposition-*
	- config_name: presupposition_both_presupposition
	data_files:
	- split: both_presupposition
	path: presupposition_both_presupposition/both_presupposition-*
	- config_name: presupposition_change_of_state
	data_files:
	- split: change_of_state
	path: presupposition_change_of_state/change_of_state-*
	- config_name: presupposition_cleft_existence
	data_files:
	- split: cleft_existence
	path: presupposition_cleft_existence/cleft_existence-*
	- config_name: presupposition_cleft_uniqueness
	data_files:
	- split: cleft_uniqueness
	path: presupposition_cleft_uniqueness/cleft_uniqueness-*
	- config_name: presupposition_only_presupposition
	data_files:
	- split: only_presupposition
	path: presupposition_only_presupposition/only_presupposition-*
	- config_name: presupposition_possessed_definites_existence
	data_files:
	- split: possessed_definites_existence
	path: presupposition_possessed_definites_existence/possessed_definites_existence-*
	- config_name: presupposition_possessed_definites_uniqueness
	data_files:
	- split: possessed_definites_uniqueness
	path: presupposition_possessed_definites_uniqueness/possessed_definites_uniqueness-*
	- config_name: presupposition_question_presupposition
	data_files:
	- split: question_presupposition
	path: presupposition_question_presupposition/question_presupposition-*
	---

	# Dataset Card for IMPPRES

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [Github](https://github.com/facebookresearch/Imppres)
	- Repository: [Github](https://github.com/facebookresearch/Imppres)
	- Paper: [Aclweb](https://www.aclweb.org/anthology/2020.acl-main.768)
	- Leaderboard:
	- Point of Contact:

	### Dataset Summary
	Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.

	### Supported Tasks and Leaderboards

	Natural Language Inference.

	### Languages

	English.

	## Dataset Structure

	### Data Instances

	The data consists of 2 configurations: implicature and presupposition.
	Each configuration consists of several different sub-datasets:

	Pressupposition
	- all_n_presupposition
	- change_of_state
	- cleft_uniqueness
	- possessed_definites_existence
	- question_presupposition
	- both_presupposition
	- cleft_existence
	- only_presupposition
	- possessed_definites_uniqueness

	Implicature
	- connectives
	- gradable_adjective
	- gradable_verb
	- modals
	- numerals_10_100
	- numerals_2_3
	- quantifiers

	Each sentence type in IMPPRES is generated according to a template that specifies the linear order of the constituents in the sentence. The constituents are sampled from a vocabulary of over 3000 lexical items annotated with grammatical features needed to ensure wellformedness. We semiautomatically generate IMPPRES using a codebase developed by Warstadt et al. (2019a) and significantly expanded for the BLiMP dataset (Warstadt et al., 2019b).

	Here is an instance of the raw presupposition data from any sub-dataset:
	```buildoutcfg
	{
	"sentence1": "All ten guys that proved to boast might have been divorcing.",
	"sentence2": "There are exactly ten guys that proved to boast.",
	"trigger": "modal",
	"presupposition": "positive",
	"gold_label": "entailment",
	"UID": "all_n_presupposition",
	"pairID": "9e",
	"paradigmID": 0
	}
	```
	and the raw implicature data from any sub-dataset:
	```buildoutcfg
	{
	"sentence1": "That teenager couldn't yell.",
	"sentence2": "That teenager could yell.",
	"gold_label_log": "contradiction",
	"gold_label_prag": "contradiction",
	"spec_relation": "negation",
	"item_type": "control",
	"trigger": "modal",
	"lexemes": "can - have to"
	}
	```
	### Data Fields
	Presupposition

	There is a slight mapping from the raw data fields in the presupposition sub-datasets and the fields appearing in the HuggingFace Datasets.
	When dealing with the HF Dataset, the following mapping of fields happens:
	```buildoutcfg
	"premise" -> "sentence1"
	"hypothesis"-> "sentence2"
	"trigger" -> "trigger" or "Not_In_Example"
	"trigger1" -> "trigger1" or "Not_In_Example"
	"trigger2" -> "trigger2" or "Not_In_Example"
	"presupposition" -> "presupposition" or "Not_In_Example"
	"gold_label" -> "gold_label"
	"UID" -> "UID"
	"pairID" -> "pairID"
	"paradigmID" -> "paradigmID"
	```
	For the most part, the majority of the raw fields remain unchanged. However, when it comes to the various `trigger` fields, a new mapping was introduced.
	There are some examples in the dataset that only have the `trigger` field while other examples have the `trigger1` and `trigger2` field without the `trigger` or `presupposition` field.
	Nominally, most examples look like the example in the Data Instances section above. Occassionally, however, some examples will look like:
	```buildoutcfg
	{
	'sentence1': 'Did that committee know when Lissa walked through the cafe?',
	'sentence2': 'That committee knew when Lissa walked through the cafe.',
	'trigger1': 'interrogative',
	'trigger2': 'unembedded',
	'gold_label': 'neutral',
	'control_item': True,
	'UID': 'question_presupposition',
	'pairID': '1821n',
	'paradigmID': 95
	}
	```
	In this example, `trigger1` and `trigger2` appear and `presupposition` and `trigger` are removed. This maintains the length of the dictionary.
	To account for these examples, we have thus introduced the mapping above such that all examples accessed through the HF Datasets interface will have the same size as well as the same fields.
	In the event that an example does not have a value for one of the fields, the field is maintained in the dictionary but given a value of `Not_In_Example`.

	To illustrate this point, the example given in the Data Instances section above would look like the following in the HF Datasets:
	```buildoutcfg
	{
	"premise": "All ten guys that proved to boast might have been divorcing.",
	"hypothesis": "There are exactly ten guys that proved to boast.",
	"trigger": "modal",
	"trigger1": "Not_In_Example",
	"trigger2": "Not_In_Example"
	"presupposition": "positive",
	"gold_label": "entailment",
	"UID": "all_n_presupposition",
	"pairID": "9e",
	"paradigmID": 0
	}
	```

	Below is description of the fields:
	```buildoutcfg
	"premise": The premise.
	"hypothesis": The hypothesis.
	"trigger": A detailed discussion of trigger types appears in the paper.
	"trigger1": A detailed discussion of trigger types appears in the paper.
	"trigger2": A detailed discussion of trigger types appears in the paper.
	"presupposition": positive or negative.
	"gold_label": Corresponds to entailment, contradiction, or neutral.
	"UID": Unique id.
	"pairID": Sentence pair ID.
	"paradigmID": ?
	```
	It is not immediately clear what the difference is between `trigger`, `trigger1`, and `trigger2` is or what the `paradigmID` refers to.

	Implicature

	The `implicature` fields only have the mapping below:
	```buildoutcfg
	"premise" -> "sentence1"
	"hypothesis"-> "sentence2"
	```
	Here is a description of the fields:
	```buildoutcfg
	"premise": The premise.
	"hypothesis": The hypothesis.
	"gold_label_log": Gold label for a logical reading of the sentence pair.
	"gold_label_prag": Gold label for a pragmatic reading of the sentence pair.
	"spec_relation": ?
	"item_type": ?
	"trigger": A detailed discussion of trigger types appears in the paper.
	"lexemes": ?
	```

	### Data Splits

	As the dataset was created to test already trained models, the only split that exists is for testing.


	## Dataset Creation

	### Curation Rationale

	IMPPRES was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.

	### 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?

	The annotations were generated semi-automatically.

	### 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

	IMPPRES is available under a Creative Commons Attribution-NonCommercial 4.0 International Public License ("The License"). You may not use these files except in compliance with the License. Please see the LICENSE file for more information before you use the dataset.

	### Citation Information

	```buildoutcfg
	@inproceedings{jeretic-etal-2020-natural,
	title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}",
	author = "Jereti\v{c}, Paloma and
	Warstadt, Alex and
	Bhooshan, Suvrat and
	Williams, Adina",
	booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
	month = jul,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.acl-main.768",
	doi = "10.18653/v1/2020.acl-main.768",
	pages = "8690--8705",
	abstract = "Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by {``}some{''} as entailments. For some presupposition triggers like {``}only{''}, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.",
	}
	```

	### Contributions

	Thanks to [@aclifton314](https://github.com/aclifton314) for adding this dataset.