Practical Comparable Data Collection for Low-Resource Languages via Images

• URL: http://arxiv.org/abs/2004.11954v2
• Date: Tue, 28 Apr 2020 19:51:43 GMT
• Title: Practical Comparable Data Collection for Low-Resource Languages via Images
• Authors: Aman Madaan, Shruti Rijhwani, Antonios Anastasopoulos, Yiming Yang, Graham Neubig
• Abstract summary: 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.
• Score: 126.64069379167975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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