论文标题
在神经机器翻译上结合单词和字符矢量表示
Combining Word and Character Vector Representation on Neural Machine Translation
论文作者
论文摘要
本文介绍了英语印度语神经机器翻译（NMT）中单词矢量表示和字符矢量表示的组合。建立了NMT模型的六种配置，具有不同的输入矢量表示：基于单词的，使用双向LSTM（BI-LSTM）（BI-LSTM）的单词和角色表示的组合，使用CNN的单词和角色表示的组合，通过三个不同的矢量操作结合Bi-LSTM和CNN的单词和字符的组合：添加：添加：添加，点式式乘法。实验结果表明，具有单词和字符表示串联的NMT模型的BLEU得分高于基线模型，范围从9.14点到11.65分，对于所有结合单词和字符表示的模型，除了模型相结合的模型外，使用bi-lstM和cnn组合单词和角色表示。与基线模型的30.83相比，获得的最高BLEU分数为42.48。
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.
