Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
machine-generated
Annotations Creators:
machine-generated
Source Datasets:
original
Tags:
License:
metadata
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: 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: 458492
num_examples: 1900
download_size: 335088
dataset_size: 458492
- 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: 432792
num_examples: 1900
download_size: 335088
dataset_size: 432792
- 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: 308627
num_examples: 1900
download_size: 335088
dataset_size: 308627
- 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: 363238
num_examples: 1900
download_size: 335088
dataset_size: 363238
- 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: 388779
num_examples: 1900
download_size: 335088
dataset_size: 388779
- 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: 349018
num_examples: 1900
download_size: 335088
dataset_size: 349018
- 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: 362334
num_examples: 1900
download_size: 335088
dataset_size: 362334
- 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: 459403
num_examples: 1900
download_size: 335088
dataset_size: 459403
- 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: 397227
num_examples: 1900
download_size: 335088
dataset_size: 397227
- 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: 221868
num_examples: 1200
download_size: 335088
dataset_size: 221868
- 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: 153672
num_examples: 1200
download_size: 335088
dataset_size: 153672
- 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:
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'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
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num_examples: 1200
download_size: 335088
dataset_size: 180702
- 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: 178560
num_examples: 1200
download_size: 335088
dataset_size: 178560
- 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:
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'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: 208620
num_examples: 1200
download_size: 335088
dataset_size: 208620
- config_name: implicature_numerals_2_3
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: gold_label_log
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class_label:
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'0': entailment
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'1': neutral
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- name: lexemes
dtype: string
splits:
- name: numerals_2_3
num_bytes: 188784
num_examples: 1200
download_size: 335088
dataset_size: 188784
- config_name: implicature_quantifiers
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: gold_label_log
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class_label:
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'0': entailment
'1': neutral
'2': contradiction
- name: gold_label_prag
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'0': entailment
'1': neutral
'2': contradiction
- name: spec_relation
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- name: item_type
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- name: trigger
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- name: lexemes
dtype: string
splits:
- name: quantifiers
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num_examples: 1200
download_size: 335088
dataset_size: 176814
Dataset Card for IMPPRES
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
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:
{
"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:
{
"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:
"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:
{
'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:
{
"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:
"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:
"premise" -> "sentence1"
"hypothesis"-> "sentence2"
Here is a description of the fields:
"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
@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 for adding this dataset.