Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Size Categories:
unknown
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
extended|xnli
ArXiv:
Tags:
License:
metadata
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ay
- bzd
- cni
- gn
- hch
- nah
- oto
- qu
- shp
- tar
license: cc-by-sa-4.0
multilinguality:
- multilingual
- translation
size_categories:
- unknown
source_datasets:
- extended|xnli
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages.'
dataset_info:
- config_name: all_languages
features:
- name: language
dtype: string
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 1129080
num_examples: 6457
- name: test
num_bytes: 1210579
num_examples: 7486
download_size: 791239
dataset_size: 2339659
- config_name: aym
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 117530
num_examples: 743
- name: test
num_bytes: 115251
num_examples: 750
download_size: 87882
dataset_size: 232781
- config_name: bzd
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 143354
num_examples: 743
- name: test
num_bytes: 127676
num_examples: 750
download_size: 91039
dataset_size: 271030
- config_name: cni
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 113256
num_examples: 658
- name: test
num_bytes: 116284
num_examples: 750
download_size: 78899
dataset_size: 229540
- config_name: gn
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 115135
num_examples: 743
- name: test
num_bytes: 101948
num_examples: 750
download_size: 80429
dataset_size: 217083
- config_name: hch
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 127966
num_examples: 743
- name: test
num_bytes: 120857
num_examples: 750
download_size: 90748
dataset_size: 248823
- config_name: nah
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 50741
num_examples: 376
- name: test
num_bytes: 102953
num_examples: 738
download_size: 56953
dataset_size: 153694
- config_name: oto
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 27010
num_examples: 222
- name: test
num_bytes: 119650
num_examples: 748
download_size: 57849
dataset_size: 146660
- config_name: quy
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 125636
num_examples: 743
- name: test
num_bytes: 112750
num_examples: 750
download_size: 85673
dataset_size: 238386
- config_name: shp
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 124500
num_examples: 743
- name: test
num_bytes: 118934
num_examples: 750
download_size: 85544
dataset_size: 243434
- config_name: tar
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 139496
num_examples: 743
- name: test
num_bytes: 122624
num_examples: 750
download_size: 89683
dataset_size: 262120
configs:
- config_name: all_languages
data_files:
- split: validation
path: all_languages/validation-*
- split: test
path: all_languages/test-*
- config_name: aym
data_files:
- split: validation
path: aym/validation-*
- split: test
path: aym/test-*
- config_name: bzd
data_files:
- split: validation
path: bzd/validation-*
- split: test
path: bzd/test-*
- config_name: cni
data_files:
- split: validation
path: cni/validation-*
- split: test
path: cni/test-*
- config_name: gn
data_files:
- split: validation
path: gn/validation-*
- split: test
path: gn/test-*
- config_name: hch
data_files:
- split: validation
path: hch/validation-*
- split: test
path: hch/test-*
- config_name: nah
data_files:
- split: validation
path: nah/validation-*
- split: test
path: nah/test-*
- config_name: oto
data_files:
- split: validation
path: oto/validation-*
- split: test
path: oto/test-*
- config_name: quy
data_files:
- split: validation
path: quy/validation-*
- split: test
path: quy/test-*
- config_name: shp
data_files:
- split: validation
path: shp/validation-*
- split: test
path: shp/test-*
- config_name: tar
data_files:
- split: validation
path: tar/validation-*
- split: test
path: tar/test-*
Dataset Card for AmericasNLI
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: [Needs More Information]
- Repository: https://github.com/abteen/americasnli
- Repository: https://github.com/nala-cub/AmericasNLI
- Paper: https://arxiv.org/abs/2104.08726
- Leaderboard: [Needs More Information]
- Point of Contact: [Needs More Information]
Dataset Summary
AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).
Supported Tasks and Leaderboards
[Needs More Information]
Languages
- aym
- bzd
- cni
- gn
- hch
- nah
- oto
- quy
- shp
- tar
Dataset Structure
Data Instances
all_languages
An example of the test split looks as follows:
{'language': 'aym', 'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix p
arlxapxti.', 'label': 2}
aym
An example of the test split looks as follows:
{'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix parlxapxti.', 'label
': 2}
bzd
An example of the test split looks as follows:
{'premise': "Bua', kèq ye' kũ e' bikeitsök erë ye' chkénãwã tã ye' ujtémĩne ie' tã páxlĩnẽ.", 'hypothesis': "Kèq ye' ùtẽnẽ ie' tã páxlĩ.", 'label': 2}
cni
An example of the test split looks as follows:
{'premise': 'Kameetsa, tee nokenkeshireajeroji, iro kantaincha tee nomateroji aisati nintajaro noñanatajiri iroakera.', 'hypothesis': 'Tee noñatajeriji.', 'label': 2}
gn
An example of the test split looks as follows:
{'premise': "Néi, ni napensaikurihína upéva rehe, ajepichaiterei ha añepyrûjey añe'ê hendive.", 'hypothesis': "Nañe'êvéi hendive.", 'label': 2}
hch
An example of the test split looks as follows:
{'premise': 'mu hekwa.', 'hypothesis': 'neuka tita xatawe m+k+ mat+a.', 'label': 2}
nah
An example of the test split looks as follows:
{'premise': 'Cualtitoc, na axnimoihliaya ino, nicualaniztoya queh naha nicamohuihqui', 'hypothesis': 'Ayoc nicamohuihtoc', 'label': 2}
oto
An example of the test split looks as follows:
{'premise': 'mi-ga, nin mibⴘy mbô̮nitho ane guenu, guedi mibⴘy nho ⴘnmⴘy xi di mⴘdi o ñana nen nⴘua manaigui', 'hypothesis': 'hin din bi pengui nen nⴘa', 'label': 2}
quy
An example of the test split looks as follows:
.', 'label': 2}
shp
An example of the test split looks as follows:
{'premise': 'Jakon riki, ja shinanamara ea ike, ikaxbi kikin frustradara ea ike jakopira ea jabe yoyo iribake.', 'hypothesis': 'Eara jabe yoyo iribiama iki.', 'label': 2}
tar
An example of the test split looks as follows:
{'premise': 'Ga’lá ju, ke tási newalayé nejé echi kítira, we ne majáli, a’lí ko uchécho ne yua ku ra’íchaki.', 'hypothesis': 'Tási ne uchecho yua ra’ícha échi rejói.', 'label': 2}
Data Fields
all_languages
- language: a multilingual string variable, with languages including ar, bg, de, el, en.
- premise: a multilingual string variable, with languages including ar, bg, de, el, en.
- hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
aym
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
bzd
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
cni
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
hch
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
nah
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
oto
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
quy
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
shp
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
tar
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
Data Splits
| Language | ISO | Family | Dev | Test |
|---|---|---|---|---|
| all_languages | -- | -- | 6457 | 7486 |
| Aymara | aym | Aymaran | 743 | 750 |
| Ashaninka | cni | Arawak | 658 | 750 |
| Bribri | bzd | Chibchan | 743 | 750 |
| Guarani | gn | Tupi-Guarani | 743 | 750 |
| Nahuatl | nah | Uto-Aztecan | 376 | 738 |
| Otomi | oto | Oto-Manguean | 222 | 748 |
| Quechua | quy | Quechuan | 743 | 750 |
| Raramuri | tar | Uto-Aztecan | 743 | 750 |
| Shipibo-Konibo | shp | Panoan | 743 | 750 |
| Wixarika | hch | Uto-Aztecan | 743 | 750 |
Dataset Creation
Curation Rationale
[Needs More Information]
Source Data
The authors translate from the Spanish subset of XNLI.
AmericasNLI is the translation of a subset of XNLI (Conneau et al., 2018). As translators between Spanish and the target languages are more frequently available than those for English, we translate from the Spanish version.
As per paragraph 3.1 of the original paper.
Initial Data Collection and Normalization
[Needs More Information]
Who are the source language producers?
[Needs More Information]
Annotations
Annotation process
The dataset comprises expert translations from Spanish XNLI.
Additionally, some translators reported that code-switching is often used to describe certain topics, and, while many words without an exact equivalence in the target language are worked in through translation or interpretation, others are kept in Spanish. To minimize the amount of Spanish vocabulary in the translated examples, we choose sentences from genres that we judged to be relatively easy to translate into the target languages: “face-to-face,” “letters,” and “telephone.”
As per paragraph 3.1 of the original paper.
Who are the annotators?
[Needs More Information]
Personal and Sensitive Information
[Needs More Information]
Considerations for Using the Data
Social Impact of Dataset
[Needs More Information]
Discussion of Biases
[Needs More Information]
Other Known Limitations
[Needs More Information]
Additional Information
Dataset Curators
[Needs More Information]
Licensing Information
Creative Commons Attribution Share Alike 4.0 International: https://github.com/abteen/americasnli/blob/main/LICENSE.md
Citation Information
@inproceedings{ebrahimi-etal-2022-americasnli,
title = "{A}mericas{NLI}: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages",
author = "Ebrahimi, Abteen and
Mager, Manuel and
Oncevay, Arturo and
Chaudhary, Vishrav and
Chiruzzo, Luis and
Fan, Angela and
Ortega, John and
Ramos, Ricardo and
Rios, Annette and
Meza Ruiz, Ivan Vladimir and
Gim{\'e}nez-Lugo, Gustavo and
Mager, Elisabeth and
Neubig, Graham and
Palmer, Alexis and
Coto-Solano, Rolando and
Vu, Thang and
Kann, Katharina",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.435",
pages = "6279--6299",
abstract = "Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-level, semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 Indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. Additionally, we explore model adaptation via continued pretraining and provide an analysis of the dataset by considering hypothesis-only models. We find that XLM-R{'}s zero-shot performance is poor for all 10 languages, with an average performance of 38.48{\%}. Continued pretraining offers improvements, with an average accuracy of 43.85{\%}. Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 49.12{\%}.",
}
Contributions
Thanks to @fdschmidt93 for adding this dataset.