--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ay - bzd - cni - gn - hch - nah - oto - qu - shp - tar license: - unknown multilinguality: - multilingual - translation pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages.' size_categories: - unknown source_datasets: - extended|xnli task_categories: - text-classification task_ids: - natural-language-inference dataset_info: - 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: test num_bytes: 115259 num_examples: 750 - name: validation num_bytes: 117538 num_examples: 743 download_size: 2256093 dataset_size: 232797 - 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: test num_bytes: 127684 num_examples: 750 - name: validation num_bytes: 143362 num_examples: 743 download_size: 2256093 dataset_size: 271046 - 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: test num_bytes: 116292 num_examples: 750 - name: validation num_bytes: 113264 num_examples: 658 download_size: 2256093 dataset_size: 229556 - 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: test num_bytes: 101956 num_examples: 750 - name: validation num_bytes: 115143 num_examples: 743 download_size: 2256093 dataset_size: 217099 - 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: test num_bytes: 120865 num_examples: 750 - name: validation num_bytes: 127974 num_examples: 743 download_size: 2256093 dataset_size: 248839 - 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: test num_bytes: 102961 num_examples: 738 - name: validation num_bytes: 50749 num_examples: 376 download_size: 2256093 dataset_size: 153710 - 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: test num_bytes: 119658 num_examples: 748 - name: validation num_bytes: 27018 num_examples: 222 download_size: 2256093 dataset_size: 146676 - 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: test num_bytes: 112758 num_examples: 750 - name: validation num_bytes: 125644 num_examples: 743 download_size: 2256093 dataset_size: 238402 - 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: test num_bytes: 118942 num_examples: 750 - name: validation num_bytes: 124508 num_examples: 743 download_size: 2256093 dataset_size: 243450 - 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: test num_bytes: 122632 num_examples: 750 - name: validation num_bytes: 139504 num_examples: 743 download_size: 2256093 dataset_size: 262136 - 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: test num_bytes: 1210591 num_examples: 7486 - name: validation num_bytes: 1129092 num_examples: 6457 download_size: 2256093 dataset_size: 2339683 --- # Dataset Card for AmericasNLI ## 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:** 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: ``` {'premise': 'Allinmi, manam chaypiqa hamutachkarqanichu, ichaqa manam allinchu tarikurqani chaymi kaqllamanta paywan rimarqani.', 'hypothesis': 'Manam paywanqa kaqllamantaqa rimarqani .', '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](https://arxiv.org/abs/2104.08726). #### 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](https://arxiv.org/abs/2104.08726). #### 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 [Needs More Information] ### Citation Information ``` @article{DBLP:journals/corr/abs-2104-08726, author = {Abteen Ebrahimi and Manuel Mager and Arturo Oncevay and Vishrav Chaudhary and Luis Chiruzzo and Angela Fan and John Ortega and Ricardo Ramos and Annette Rios and Ivan Vladimir and Gustavo A. Gim{\'{e}}nez{-}Lugo and Elisabeth Mager and Graham Neubig and Alexis Palmer and Rolando A. Coto Solano and Ngoc Thang Vu and Katharina Kann}, title = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages}, journal = {CoRR}, volume = {abs/2104.08726}, year = {2021}, url = {https://arxiv.org/abs/2104.08726}, eprinttype = {arXiv}, eprint = {2104.08726}, timestamp = {Mon, 26 Apr 2021 17:25:10 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@fdschmidt93](https://github.com/fdschmidt93) for adding this dataset.