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English to Bengali Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3ASI2YQIXC" target="_blank" >RIV/00216208:11320/22:SI2YQIXC - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.wat-1.14" target="_blank" >https://aclanthology.org/2022.wat-1.14</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    English to Bengali Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation

  • Original language description

    Automatic translation of one natural language to another is a popular task of natural language processing. Although the deep learning-based technique known as neural machine translation (NMT) is a widely accepted machine translation approach, it needs an adequate amount of training data, which is a challenging issue for low-resource pair translation. Moreover, the multimodal concept utilizes text and visual features to improve low-resource pair translation. WAT2022 (Workshop on Asian Translation 2022) organizes (hosted by the COLING 2022) English to Bengali multimodal translation task where we have participated as a team named CNLP-NITS-PP in two tracks: 1) text-only and 2) multimodal translation. Herein, we have proposed a transliteration-based phrase pairs augmentation approach which shows improvement in the multimodal translation task and achieved benchmark results on Bengali Visual Genome 1.0 dataset. We have attained the best results on the challenge and evaluation test set for English to Bengali multimodal translation with BLEU scores of 28.70, 43.90 and RIBES scores of 0.688931, 0.780669, respectively.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 9th Workshop on Asian Translation

  • ISBN

  • ISSN

    2951-2093

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    111-116

  • Publisher name

    International Conference on Computational Linguistics

  • Place of publication

  • Event location

    Gyeongju, Republic of Korea

  • Event date

    Jan 1, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article