BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages

• URL: http://arxiv.org/abs/2412.04351v2
• Date: Thu, 02 Jan 2025 16:33:40 GMT
• Title: BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages
• Authors: Vandan Mujadia, Dipti Misra Sharma,
• Abstract summary: This paper focuses on developing translation models and related applications for 36 Indian languages. It addresses challenges like script variations, phonetic differences, and syntactic diversity. It proposes strategies for corpus creation by leveraging existing resources, developing parallel datasets, generating domain-specific corpora, and utilizing synthetic data techniques.
• Score: 4.1101087490516575
• License:
• Abstract: This paper focuses on developing translation models and related applications for 36 Indian languages, including Assamese, Awadhi, Bengali, Bhojpuri, Braj, Bodo, Dogri, English, Konkani, Gondi, Gujarati, Hindi, Hinglish, Ho, Kannada, Kangri, Kashmiri (Arabic and Devanagari), Khasi, Mizo, Magahi, Maithili, Malayalam, Marathi, Manipuri (Bengali and Meitei), Nepali, Oriya, Punjabi, Sanskrit, Santali, Sinhala, Sindhi (Arabic and Devanagari), Tamil, Tulu, Telugu, and Urdu. Achieving this requires parallel and other types of corpora for all 36 * 36 language pairs, addressing challenges like script variations, phonetic differences, and syntactic diversity. For instance, languages like Kashmiri and Sindhi, which use multiple scripts, demand script normalization for alignment, while low-resource languages such as Khasi and Santali require synthetic data augmentation to ensure sufficient coverage and quality. To address these challenges, this work proposes strategies for corpus creation by leveraging existing resources, developing parallel datasets, generating domain-specific corpora, and utilizing synthetic data techniques. Additionally, it evaluates machine translation across various dimensions, including standard and discourse-level translation, domain-specific translation, reference-based and reference-free evaluation, error analysis, and automatic post-editing. By integrating these elements, the study establishes a comprehensive framework to improve machine translation quality and enable better cross-lingual communication in India's linguistically diverse ecosystem.

Related papers

• CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving [61.73180469072787]
  We focus on the problem of spoken translation (ST) of code-switched speech in Indian languages to English text. We present a new end-to-end model architecture COSTA that scaffolds on pretrained automatic speech recognition (ASR) and machine translation (MT) modules. COSTA significantly outperforms many competitive cascaded and end-to-end multimodal baselines by up to 3.5 BLEU points.
  arXiv  Detail & Related papers  (2024-06-16T16:10:51Z)
• 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)
• Breaking Language Barriers: A Question Answering Dataset for Hindi and Marathi [1.03590082373586]
  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.
  arXiv  Detail & Related papers  (2023-08-19T00:39:21Z)
• 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)
• "A Passage to India": Pre-trained Word Embeddings for Indian Languages [30.607474624873014]
  We use various existing approaches to create multiple word embeddings for 14 Indian languages. We place these embeddings for all these languages in a single repository. We release a total of 436 models using 8 different approaches.
  arXiv  Detail & Related papers  (2021-12-27T17:31:04Z)
• Challenge Dataset of Cognates and False Friend Pairs from Indian Languages [54.6340870873525]
  Cognates are present in multiple variants of the same text across different languages. In this paper, we describe the creation of two cognate datasets for twelve Indian languages.
  arXiv  Detail & Related papers  (2021-12-17T14:23:43Z)
• Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages [50.82410844837726]
  We demonstrate the use of cross-lingual word embeddings for detecting cognates among fourteen Indian languages. We evaluate our methods to detect cognates on a challenging dataset of twelve Indian languages. We observe an improvement of up to 18% points, in terms of F-score, for cognate detection.
  arXiv  Detail & Related papers  (2021-12-16T11:17:58Z)
• 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)
• A Multilingual Parallel Corpora Collection Effort for Indian Languages [43.62422999765863]
  We present sentence aligned parallel corpora across 10 Indian languages - Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English. The corpora are compiled from online sources which have content shared across languages.
  arXiv  Detail & Related papers  (2020-07-15T14:00:18Z)

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.