midas/bbc_hindi_nli

---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- hi
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|bbc__hindi_news_classification
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: BBC Hindi NLI Dataset
dataset_info:
config_name: bbc hindi nli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': not-entailment
'1': entailment
- name: topic
dtype:
class_label:
names:
'0': india
'1': news
'2': international
'3': entertainment
'4': sport
'5': science
splits:
- name: train
num_bytes: 2990064
num_examples: 15552
- name: validation
num_bytes: 496800
num_examples: 2580
- name: test
num_bytes: 494424
num_examples: 2592
download_size: 309124
dataset_size: 3981288
configs:
- config_name: bbc hindi nli
data_files:
- split: train
path: bbc hindi nli/train-*
- split: validation
path: bbc hindi nli/validation-*
- split: test
path: bbc hindi nli/test-*
default: true
---

# Dataset Card for BBC Hindi NLI Dataset

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

## Dataset Description

- **Repository:** [GitHub](https://github.com/midas-research/hindi-nli-data)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71)
- **Point of Contact:** [GitHub](https://github.com/midas-research/hindi-nli-data)

### Dataset Summary

- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]

### Supported Tasks and Leaderboards

- Natural Language Inference for Hindi

### Languages

Dataset is in Hindi

## Dataset Structure

- Data is structured in TSV format.
- Train and Test files are in seperate files

### Dataset Instances

An example of 'train' looks as follows.

```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}

```
### Data Fields

- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.

### Data Splits

- Train : 15553
- Valid : 2581
- Test : 2593

## Dataset Creation

- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"

### Source Data

Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)

#### Initial Data Collection and Normalization

- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.

#### Who are the source language producers?

Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"

### Annotations

#### Annotation process

Annotation process has been described in Dataset Creation Section.

#### Who are the annotators?

Annotation is done automatically.

### Personal and Sensitive Information

No Personal and Sensitive Information is mentioned in the Datasets.

## Considerations for Using the Data

Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71

### Discussion of Biases

Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71

### Other Known Limitations

No other known limitations

## Additional Information

Pls refer to this link: https://github.com/midas-research/hindi-nli-data

### Dataset Curators

It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to [e-mail].
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.

### Licensing Information

Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.

### Citation Information

```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```

### Contributions

Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset.

原始信息汇总

数据集概述

名称: BBC Hindi NLI Dataset

语言: 印地语 (Hindi)

许可证: MIT

多语言性: 单语种

大小: 10K<n<100K

来源数据集: 扩展自BBC Hindi News Classification

任务类别: 文本分类

任务ID: 自然语言推理

数据集结构

配置名称: bbc hindi nli

特征:

• premise: 字符串类型
• hypothesis: 字符串类型
• label: 类别标签，包括0: not-entailment, 1: entailment
• topic: 类别标签，包括0: india, 1: news, 2: international, 3: entertainment, 4: sport, 5: science

数据分割:

• 训练集: 15552个样本，2990064字节
• 验证集: 2580个样本，496800字节
• 测试集: 2592个样本，494424字节

数据集创建

注释创建者: 机器生成

语言创建者: 发现

来源数据: 使用Poliak等人的重构技术，将公开的BBC Hindi新闻文本分类数据集转换为文本蕴含问题。通过构建每个类别标签分类法中的模板假设，并与原始注释句子配对，创建文本蕴含样本。

初始数据收集和规范化: 原始数据集包含4,335个印地语新闻标题，分为14个类别。经过处理，将相关但低流行度的类别合并，并移除了样本较少的类别。

注释过程: 自动完成。

使用数据注意事项

个人和敏感信息: 数据集中未提及个人和敏感信息。

其他已知限制: 无其他已知限制。