Breaking Language Barriers: A Question Answering Dataset for Hindi and Marathi

• URL: http://arxiv.org/abs/2308.09862v3
• Date: Sat, 17 Feb 2024 07:02:26 GMT
• Title: Breaking Language Barriers: A Question Answering Dataset for Hindi and Marathi
• Authors: Maithili Sabane and Onkar Litake and Aman Chadha
• Abstract summary: This paper focuses on developing a Question Answering dataset for two such languages- Hindi and Marathi. Despite Hindi being the 3rd most spoken language worldwide, and Marathi being the 11th most spoken language globally, both languages face limited resources for building efficient Question Answering systems. We release the largest Question-Answering dataset available for these languages, with each dataset containing 28,000 samples.
• Score: 1.03590082373586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent advances in deep-learning have led to the development of highly sophisticated systems with an unquenchable appetite for data. On the other hand, building good deep-learning models for low-resource languages remains a challenging task. This paper focuses on developing a Question Answering dataset for two such languages- Hindi and Marathi. Despite Hindi being the 3rd most spoken language worldwide, with 345 million speakers, and Marathi being the 11th most spoken language globally, with 83.2 million speakers, both languages face limited resources for building efficient Question Answering systems. To tackle the challenge of data scarcity, we have developed a novel approach for translating the SQuAD 2.0 dataset into Hindi and Marathi. We release the largest Question-Answering dataset available for these languages, with each dataset containing 28,000 samples. We evaluate the dataset on various architectures and release the best-performing models for both Hindi and Marathi, which will facilitate further research in these languages. Leveraging similarity tools, our method holds the potential to create datasets in diverse languages, thereby enhancing the understanding of natural language across varied linguistic contexts. Our fine-tuned models, code, and dataset will be made publicly available.

Related papers

• BhasaAnuvaad: A Speech Translation Dataset for 13 Indian Languages [27.273651323572786]
  We evaluate the performance of widely-used Automatic Speech Translation systems on Indian languages. There is a striking absence of systems capable of accurately translating colloquial and informal language. We introduce BhasaAnuvaad, the largest publicly available dataset for AST involving 13 out of 22 scheduled Indian languages and English.
  arXiv  Detail & Related papers  (2024-11-07T13:33:34Z)
• A multilingual dataset for offensive language and hate speech detection for hausa, yoruba and igbo languages [0.0]
  This study addresses the challenge by developing and introducing novel datasets for offensive language detection in three major Nigerian languages: Hausa, Yoruba, and Igbo. We collected data from Twitter and manually annotated it to create datasets for each of the three languages, using native speakers. We used pre-trained language models to evaluate their efficacy in detecting offensive language in our datasets. The best-performing model achieved an accuracy of 90%.
  arXiv  Detail & Related papers  (2024-06-04T09:58:29Z)
• Open the Data! Chuvash Datasets [50.59120569845975]
  We introduce four comprehensive datasets for the Chuvash language. These datasets include a monolingual dataset, a parallel dataset with Russian, a parallel dataset with English, and an audio dataset.
  arXiv  Detail & Related papers  (2024-05-31T07:51:19Z)
• MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering [0.4194295877935868]
  This research endeavors to bridge the gap of the absence of efficient QnA datasets in low-resource languages. We introduce MahaSQuAD, the first-ever full SQuAD dataset for the Indic language Marathi, consisting of 118,516 training, 11,873 validation, and 11,803 test samples.
  arXiv  Detail & Related papers  (2024-04-20T12:16:35Z)
• Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning [49.79783940841352]
  Existing datasets are almost all in the English language. We work with fluent speakers of languages from around the world to collect natural instances of instructions and completions. We create the most extensive multilingual collection to date, comprising 513 million instances through templating and translating existing datasets across 114 languages.
  arXiv  Detail & Related papers  (2024-02-09T18:51:49Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India [33.31556860332746]
  PMIndiaSum is a multilingual and massively parallel summarization corpus focused on languages in India. Our corpus provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs.
  arXiv  Detail & Related papers  (2023-05-15T17:41:15Z)
• Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages [62.730361829175415]
  MIRACL is a multilingual dataset we have built for the WSDM 2023 Cup challenge. It focuses on ad hoc retrieval across 18 different languages. Our goal is to spur research that will improve retrieval across a continuum of languages.
  arXiv  Detail & Related papers  (2022-10-18T16:47:18Z)
• GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems [66.92182084456809]
  We introduce a novel data curation method that generates GlobalWoZ -- a large-scale multilingual ToD dataset from an English ToD dataset. Our method is based on translating dialogue templates and filling them with local entities in the target-language countries. We release our dataset as well as a set of strong baselines to encourage research on learning multilingual ToD systems for real use cases.
  arXiv  Detail & Related papers  (2021-10-14T19:33:04Z)
• Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi [2.4737119633827174]
  MOLD is the first dataset of its kind compiled for Marathi, opening a new domain for research in low-resource Indo-Aryan languages. We present results from several machine learning experiments on this dataset, including zero-short and other transfer learning experiments on state-of-the-art cross-lingual transformers.
  arXiv  Detail & Related papers  (2021-09-08T11:29:44Z)
• Multilingual and code-switching ASR challenges for low resource Indian languages [59.2906853285309]
  We focus on building multilingual and code-switching ASR systems through two different subtasks related to a total of seven Indian languages. We provide a total of 600 hours of transcribed speech data, comprising train and test sets, in these languages. We also provide a baseline recipe for both the tasks with a WER of 30.73% and 32.45% on the test sets of multilingual and code-switching subtasks, respectively.
  arXiv  Detail & Related papers  (2021-04-01T03:37:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.