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Practical Comparable Data Collection for Low-Resource Languages via Images

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arxiv 2004.11954 v2 pith:LWHOPUTQ submitted 2020-04-24 cs.CL

Aman Madaan , Shruti Rijhwani , Antonios Anastasopoulos , Yiming Yang , Graham Neubig This is my paper · ORCID
classification cs.CL
keywords languagescomparabledataimagesmethodlow-resourcepairstranslations
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We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators. Our method involves using a carefully selected set of images as a pivot between the source and target languages by getting captions for such images in both languages independently. Human evaluations on the English-Hindi comparable corpora created with our method show that 81.1% of the pairs are acceptable translations, and only 2.47% of the pairs are not translations at all. We further establish the potential of the dataset collected through our approach by experimenting on two downstream tasks - machine translation and dictionary extraction. All code and data are available at https://github.com/madaan/PML4DC-Comparable-Data-Collection.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Contrastive fine-tuning on multilingual image-caption pairs, without bitexts, improves cross-lingual sentence alignment and partially incorporates a previously unseen language.

  2. LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models

    cs.CL 2024-11 conditional novelty 3.0 of 10

    LIMBA is a proposed pipeline that combines collection, grammatical tagging, translation, speech, and generative modules to build language models for low-resource languages, with preliminary Sardinian experiments.

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