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:
          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: 180702
        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:
            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: 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
        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: 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
        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: 176814
        num_examples: 1200
    download_size: 335088
    dataset_size: 176814

Dataset Card for IMPPRES

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: Github
Repository: Github
Paper: Aclweb
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:

{
"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.

Datasets:

facebook
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imppres

Dataset Card for IMPPRES

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions