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      Combining Word and Character Vector Representation on Neural Machine Translation

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          Abstract

          This paper describes combinations of word vector representation and character vector representation in English-Indonesian neural machine translation (NMT). Six configurations of NMT models were built with different input vector representations: word-based, combination of word and character representation using bidirectional LSTM(bi-LSTM), combination of word and character representation using CNN, combination of word and character representation by combining bi-LSTM and CNN by three different vector operations: addition, pointwise multiplication, and averaging. The experiment results showed that NMT models with concatenation of word and character representation obtained BLEU score higher than baseline model, ranging from 9.14 points to 11.65 points, for all models that combining both word and character representation, except the model that combining word and character representation using both bi-LSTM and CNN by addition operation. The highest BLEU score achieved was 42.48 compared to the 30.83 of the baseline model.

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          Author and article information

          Journal
          13 September 2020
          Article
          2009.05935
          c9735259-19e6-47f3-b662-99644383bd93

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          5 pages, 5 figures. Published in 2019 Fourth International Conference on Informatics and Computing (ICIC)
          cs.CL

          Theoretical computer science
          Theoretical computer science

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