
# Dataset Card for ESsnli

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [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)

## Dataset Description

- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary

Machine-translated Spanish version of the Stanford Natural Language Inference dataset.

### Supported Tasks and Leaderboards

[Needs More Information]

### Languages

Machine-generated Spanish (from an English-language original corpus).

## Dataset Structure

### Data Instances

For each instance, there is a string for the premise, a string for the hypothesis, a string for the label given by the annotator and a string for the gold label, as well as data about the images that originated the sentences. Note that each premise may appear three times with a different hypothesis and label. See the [SNLI corpus viewer](https://huggingface.co/datasets/viewer/?dataset=snli) to explore more examples.

```
{"annotator_labels": ["contradiction"]
"captionID": "3618932839.jpg#4"
"gold_label": "contradiction"
"pairID": "3618932839.jpg#4r1c"
"sentence1": "El perro intenta saltar sobre el poste."
"sentence2": "Perrito durmiendo con su madre."}
```

### Data Fields

- `sentence1`: a string used to determine the truthfulness of the hypothesis
- `sentence2`: a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise
- `label`: a string whose value may be either "entailment", "contradiction" or "neutral".

### Data Splits

[Needs More Information]

## Dataset Creation

### Curation Rationale

This corpus was built to remedy the scarcity of annotated Spanish-language datasets for NLI.  It was generated by translating from the SNLI original dataset to Spanish using Argos. While machine translation is far from an ideal source for semantic classification, it is an aid to enlarging the data available.

### Source Data

#### Initial Data Collection and Normalization

The hypotheses (sentence1) were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015.

Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015).

The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) and the [VisualGenome corpus](https://visualgenome.org/). The photo captions used to prompt the data creation were collected on Flickr by [Young et al. (2014)](https://www.aclweb.org/anthology/Q14-1006.pdf), who extended the Flickr 8K dataset developed by [Hodosh et al. (2013)](https://www.jair.org/index.php/jair/article/view/10833). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in [MS-COCO](https://cocodataset.org/#home) and [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/).

The premises (sentence2) from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted.

#### Who are the source language producers?

A large portion of the premises (160k) were produced in the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators.

The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers.

An additional 4,000 premises come from the pilot study of the [VisualGenome corpus](https://visualgenome.org/static/paper/Visual_Genome.pdf). Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour.

### Annotations

#### Annotation process

56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015).

The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors.

#### Who are the annotators?

The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples.

### Personal and Sensitive Information

The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos.

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

The language reflects the content of the photos collected from Flickr, as described in the [Data Collection](#initial-data-collection-and-normalization) section. [Rudinger et al (2017)](https://www.aclweb.org/anthology/W17-1609.pdf) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses.

### Other Known Limitations

The translation of the sentences was mostly unsupervised and may introduce some noise in the corpus. Machine translation from an English-language corpus is likely to generate syntactic and lexical forms that differ from those a human Spanish speaker would produce.

[Gururangan et al (2018)](https://www.aclweb.org/anthology/N18-2017.pdf), [Poliak et al (2018)](https://www.aclweb.org/anthology/S18-2023.pdf), and [Tsuchiya (2018)](https://www.aclweb.org/anthology/L18-1239.pdf) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time.

## Additional Information

### Dataset Curators

The SNLI corpus was developed by Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning as part of the [Stanford NLP group](https://nlp.stanford.edu/).

It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040, the National Science Foundation under grant no. IIS 1159679, and the Department of the Navy, Office of Naval Research, under grant no. N00014-10-1-0109.

### Licensing Information

This corpus is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0).

The Stanford Natural Language Inference Corpus is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).

### Citation Information

[Needs More Information]
task_categories:text-classification task_ids:natural-language-inference annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:n<100K source_datasets:extended|snli region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "multilinguality": ["monolingual"], "size_categories": ["n<100K"], "source_datasets": ["extended|snli"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "languages": ["es"], "licenses": ["cc-by-nc-sa-4.0"]}
TAGS
#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<100K #source_datasets-extended|snli #region-us 


# Dataset Card for ESsnli

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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

## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: 

### Dataset Summary

Machine-translated Spanish version of the Stanford Natural Language Inference dataset.

### Supported Tasks and Leaderboards



### Languages

Machine-generated Spanish (from an English-language original corpus).

## Dataset Structure

### Data Instances

For each instance, there is a string for the premise, a string for the hypothesis, a string for the label given by the annotator and a string for the gold label, as well as data about the images that originated the sentences. Note that each premise may appear three times with a different hypothesis and label. See the SNLI corpus viewer to explore more examples.



### Data Fields

- 'sentence1': a string used to determine the truthfulness of the hypothesis
- 'sentence2': a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise
- 'label': a string whose value may be either "entailment", "contradiction" or "neutral".

### Data Splits



## Dataset Creation

### Curation Rationale

This corpus was built to remedy the scarcity of annotated Spanish-language datasets for NLI.  It was generated by translating from the SNLI original dataset to Spanish using Argos. While machine translation is far from an ideal source for semantic classification, it is an aid to enlarging the data available.

### Source Data

#### Initial Data Collection and Normalization

The hypotheses (sentence1) were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015.

Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015).

The corpus includes content from the Flickr 30k corpus and the VisualGenome corpus. The photo captions used to prompt the data creation were collected on Flickr by Young et al. (2014), who extended the Flickr 8K dataset developed by Hodosh et al. (2013). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in MS-COCO and YFCC100M.

The premises (sentence2) from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted.

#### Who are the source language producers?

A large portion of the premises (160k) were produced in the Flickr 30k corpus by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators.

The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers.

An additional 4,000 premises come from the pilot study of the VisualGenome corpus. Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour.

### Annotations

#### Annotation process

56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015).

The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors.

#### Who are the annotators?

The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples.

### Personal and Sensitive Information

The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos.

## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases

The language reflects the content of the photos collected from Flickr, as described in the Data Collection section. Rudinger et al (2017) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses.

### Other Known Limitations

The translation of the sentences was mostly unsupervised and may introduce some noise in the corpus. Machine translation from an English-language corpus is likely to generate syntactic and lexical forms that differ from those a human Spanish speaker would produce.

Gururangan et al (2018), Poliak et al (2018), and Tsuchiya (2018) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time.

## Additional Information

### Dataset Curators

The SNLI corpus was developed by Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning as part of the Stanford NLP group.

It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040, the National Science Foundation under grant no. IIS 1159679, and the Department of the Navy, Office of Naval Research, under grant no. N00014-10-1-0109.

### Licensing Information

This corpus is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The Stanford Natural Language Inference Corpus is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.




# Dataset Card for ESsnli ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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 ## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary

Machine-translated Spanish version of the Stanford Natural Language Inference dataset. ### Supported Tasks and Leaderboards ### Languages

Machine-generated Spanish (from an English-language original corpus). ## Dataset Structure ### Data Instances

For each instance, there is a string for the premise, a string for the hypothesis, a string for the label given by the annotator and a string for the gold label, as well as data about the images that originated the sentences. Note that each premise may appear three times with a different hypothesis and label. See the SNLI corpus viewer to explore more examples. ### Data Fields

- 'sentence1': a string used to determine the truthfulness of the hypothesis
- 'sentence2': a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise
- 'label': a string whose value may be either "entailment", "contradiction" or "neutral". ### Data Splits ## Dataset Creation ### Curation Rationale

This corpus was built to remedy the scarcity of annotated Spanish-language datasets for NLI.  It was generated by translating from the SNLI original dataset to Spanish using Argos. While machine translation is far from an ideal source for semantic classification, it is an aid to enlarging the data available. ### Source Data #### Initial Data Collection and Normalization

The hypotheses (sentence1) were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015.

Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015).

The corpus includes content from the Flickr 30k corpus and the VisualGenome corpus. The photo captions used to prompt the data creation were collected on Flickr by Young et al. (2014), who extended the Flickr 8K dataset developed by Hodosh et al. (2013). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in MS-COCO and YFCC100M.

The premises (sentence2) from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted. #### Who are the source language producers?

A large portion of the premises (160k) were produced in the Flickr 30k corpus by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators.

The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers.

An additional 4,000 premises come from the pilot study of the VisualGenome corpus. Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour. ### Annotations #### Annotation process

56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015).

The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors. #### Who are the annotators?

The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples. ### Personal and Sensitive Information

The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos. ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases

The language reflects the content of the photos collected from Flickr, as described in the Data Collection section. Rudinger et al (2017) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses. ### Other Known Limitations

The translation of the sentences was mostly unsupervised and may introduce some noise in the corpus. Machine translation from an English-language corpus is likely to generate syntactic and lexical forms that differ from those a human Spanish speaker would produce.

Gururangan et al (2018), Poliak et al (2018), and Tsuchiya (2018) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time. ## Additional Information ### Dataset Curators

The SNLI corpus was developed by Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning as part of the Stanford NLP group.

It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040, the National Science Foundation under grant no. IIS 1159679, and the Department of the Navy, Office of Naval Research, under grant no. N00014-10-1-0109. ### Licensing Information

This corpus is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The Stanford Natural Language Inference Corpus is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
TAGS
#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<100K #source_datasets-extended|snli #region-us 
 # Dataset Card for ESsnli ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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 ## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary

Machine-translated Spanish version of the Stanford Natural Language Inference dataset. ### Supported Tasks and Leaderboards ### Languages

Machine-generated Spanish (from an English-language original corpus). ## Dataset Structure ### Data Instances

For each instance, there is a string for the premise, a string for the hypothesis, a string for the label given by the annotator and a string for the gold label, as well as data about the images that originated the sentences. Note that each premise may appear three times with a different hypothesis and label. See the SNLI corpus viewer to explore more examples. ### Data Fields

- 'sentence1': a string used to determine the truthfulness of the hypothesis
- 'sentence2': a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise
- 'label': a string whose value may be either "entailment", "contradiction" or "neutral". ### Data Splits ## Dataset Creation ### Curation Rationale

This corpus was built to remedy the scarcity of annotated Spanish-language datasets for NLI.  It was generated by translating from the SNLI original dataset to Spanish using Argos. While machine translation is far from an ideal source for semantic classification, it is an aid to enlarging the data available. ### Source Data #### Initial Data Collection and Normalization

The hypotheses (sentence1) were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015.

Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015).

The corpus includes content from the Flickr 30k corpus and the VisualGenome corpus. The photo captions used to prompt the data creation were collected on Flickr by Young et al. (2014), who extended the Flickr 8K dataset developed by Hodosh et al. (2013). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in MS-COCO and YFCC100M.

The premises (sentence2) from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted. #### Who are the source language producers?

A large portion of the premises (160k) were produced in the Flickr 30k corpus by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators.

The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers.

An additional 4,000 premises come from the pilot study of the VisualGenome corpus. Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour. ### Annotations #### Annotation process

56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015).

The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors. #### Who are the annotators?

The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples. ### Personal and Sensitive Information

The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos. ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases

The language reflects the content of the photos collected from Flickr, as described in the Data Collection section. Rudinger et al (2017) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses. ### Other Known Limitations

The translation of the sentences was mostly unsupervised and may introduce some noise in the corpus. Machine translation from an English-language corpus is likely to generate syntactic and lexical forms that differ from those a human Spanish speaker would produce.

Gururangan et al (2018), Poliak et al (2018), and Tsuchiya (2018) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time. ## Additional Information ### Dataset Curators

The SNLI corpus was developed by Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning as part of the Stanford NLP group.

It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040, the National Science Foundation under grant no. IIS 1159679, and the Department of the Navy, Office of Naval Research, under grant no. N00014-10-1-0109. ### Licensing Information

This corpus is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The Stanford Natural Language Inference Corpus is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
passage: TAGS
#task_categories-text-classification #task_ids-natural-language-inference #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-n<100K #source_datasets-extended|snli #region-us 
# Dataset Card for ESsnli## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact:### Dataset Summary

Machine-translated Spanish version of the Stanford Natural Language Inference dataset.### Supported Tasks and Leaderboards### Languages

Machine-generated Spanish (from an English-language original corpus).## Dataset Structure### Data Instances

For each instance, there is a string for the premise, a string for the hypothesis, a string for the label given by the annotator and a string for the gold label, as well as data about the images that originated the sentences. Note that each premise may appear three times with a different hypothesis and label. See the SNLI corpus viewer to explore more examples.### Data Fields

- 'sentence1': a string used to determine the truthfulness of the hypothesis
- 'sentence2': a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise
- 'label': a string whose value may be either "entailment", "contradiction" or "neutral".### Data Splits## Dataset Creation passage: ### Curation Rationale

This corpus was built to remedy the scarcity of annotated Spanish-language datasets for NLI.  It was generated by translating from the SNLI original dataset to Spanish using Argos. While machine translation is far from an ideal source for semantic classification, it is an aid to enlarging the data available.### Source Data#### Initial Data Collection and Normalization

The hypotheses (sentence1) were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015.

Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015).

The corpus includes content from the Flickr 30k corpus and the VisualGenome corpus. The photo captions used to prompt the data creation were collected on Flickr by Young et al. (2014), who extended the Flickr 8K dataset developed by Hodosh et al. (2013). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in MS-COCO and YFCC100M.

The premises (sentence2) from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted. passage: #### Who are the source language producers?

A large portion of the premises (160k) were produced in the Flickr 30k corpus by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators.

The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers.

An additional 4,000 premises come from the pilot study of the VisualGenome corpus. Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour.### Annotations#### Annotation process

56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015).

The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors.#### Who are the annotators?

The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples.### Personal and Sensitive Information

The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos.## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases

The language reflects the content of the photos collected from Flickr, as described in the Data Collection section. Rudinger et al (2017) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses.