# Datasets:imppres

Languages: English
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: machine-generated
Annotations Creators: machine-generated
Source Datasets: original
Dataset Preview
The dataset preview is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    NonStreamableDatasetError
Message:      Streaming is not possible for this dataset because data host server doesn't support HTTP range requests. You can still load this dataset in non-streaming mode by passing streaming=False (default)
Traceback:    Traceback (most recent call last):
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 419, in open
return open_files(
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 272, in open_files
fs, fs_token, paths = get_fs_token_paths(
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 586, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 253, in filesystem
return cls(**storage_options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 76, in __call__
obj = super().__call__(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 59, in __init__
self.zip = zipfile.ZipFile(
File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__
self._RealGetContents()
File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents
endrec = _EndRecData(fp)
File "/usr/local/lib/python3.9/zipfile.py", line 263, in _EndRecData
fpin.seek(0, 2)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 747, in seek
raise ValueError("Cannot seek streaming HTTP file")
ValueError: Cannot seek streaming HTTP file

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/first_rows.py", line 571, in compute_first_rows_response
rows = get_rows(
File "/src/services/worker/src/worker/job_runners/first_rows.py", line 162, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/first_rows.py", line 218, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 937, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 113, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/tmp/modules-cache/datasets_modules/datasets/imppres/fbf8113a4d51c8b21cf4e9631bec0ac5ae97d647acd461d00627d872134dac00/imppres.py", line 232, in _generate_examples
with open(filepath, encoding="utf-8") as f:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 70, in wrapper
return function(*args, use_auth_token=use_auth_token, **kwargs)
raise NonStreamableDatasetError(
datasets.download.streaming_download_manager.NonStreamableDatasetError: Streaming is not possible for this dataset because data host server doesn't support HTTP range requests. You can still load this dataset in non-streaming mode by passing streaming=False (default)

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# Dataset Card for IMPPRES

### 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.

Natural Language Inference.

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
• 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",
}


and the raw implicature data from any sub-dataset:

{
"sentence1": "That teenager couldn't yell.",
"sentence2": "That teenager could yell.",
"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"


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',
}


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",
}


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.


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.

### Annotations

#### Who are the annotators?

The annotations were generated semi-automatically.

## Considerations for Using the Data

### 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
Bhooshan, Suvrat  and
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
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.