@inproceedings{singh-etal-2021-multiple,
    title = "Multiple Captions Embellished Multilingual Multi-Modal Neural Machine Translation",
    author = "Singh, Salam Michael  and
      Sanayai Meetei, Loitongbam  and
      Singh, Thoudam Doren  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021)",
    month = sep,
    year = "2021",
    address = "Online (Virtual Mode)",
    publisher = "INCOMA Ltd.",
    url = "https://aclanthology.org/2021.mmtlrl-1.2",
    pages = "2--11",
    abstract = "Neural machine translation based on bilingual text with limited training data suffers from lexical diversity, which lowers the rare word translation accuracy and reduces the generalizability of the translation system. In this work, we utilise the multiple captions from the Multi-30K dataset to increase the lexical diversity aided with the cross-lingual transfer of information among the languages in a multilingual setup. In this multilingual and multimodal setting, the inclusion of the visual features boosts the translation quality by a significant margin. Empirical study affirms that our proposed multimodal approach achieves substantial gain in terms of the automatic score and shows robustness in handling the rare word translation in the pretext of English to/from Hindi and Telugu translation tasks.",
}