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English-to-Korean Machine Translation using Image Information

Jangseong Bae, Hyunsun Hwang, Changki Lee

http://doi.org/10.5626/JOK.2019.46.7.690

Machine translation automatically converts a text in one language into another language. Conventional machine translations use only texts for translation which is a disadvantage in that various information related to input text cannot be utilized. In recent years, multimodal machine translation models have emerged that use images related to input text as additional inputs, unlike conventional machine translations which use only textual data. In this paper, image information was added at decoding time of machine translation according to recent research trends and used for English-to-Korean automated translation. In addition, we propose a model with a decoding gate to adjust the textual and image information at the decoding time. Our experimental results show that the proposed method resulted in better performance than the non-gated model.

Addressing Low-Resource Problems in Statistical Machine Translation of Manual Signals in Sign Language

Hancheol Park, Jung-Ho Kim, Jong C. Park

http://doi.org/

Despite the rise of studies in spoken to sign language translation, low-resource problems of sign language corpus have been rarely addressed. As a first step towards translating from spoken to sign language, we addressed the problems arising from resource scarcity when translating spoken language to manual signals translation using statistical machine translation techniques. More specifically, we proposed three preprocessing methods: 1) paraphrase generation, which increases the size of the corpora, 2) lemmatization, which increases the frequency of each word in the corpora and the translatability of new input words in spoken language, and 3) elimination of function words that are not glossed into manual signals, which match the corresponding constituents of the bilingual sentence pairs. In our experiments, we used different types of English-American sign language parallel corpora. The experimental results showed that the system with each method and the combination of the methods improved the quality of manual signals translation, regardless of the type of the corpora.


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