Neural Machine Translation by Fusing Key Information of Text

Abstract

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results' quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model's performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.

Machine Translation of Electrical Terminology Constraints

“…Wu et al [20] used BERT to extract context features and proposed three ways to fuse context sequences with source language sequences. Hu et al [21] proposed that accurate and complete translation of key information in the text can ensure the quality of translation results. Their work is embodied in the fusion of key information in the source language with the source language sentences through the preset encoder, which improves the translation effect of keywords.…”

“…In summary, in the existing research on machine translation in the professional field, the main ideas are divided into three categories: (a) Mark the term position in the corpus to allow the model to enhance the learning of the marker position [9][10][11][12]20]; (b) Change the structure of the Transformer encoder to enhance the learning of term information [21][22][23]25,31]; (c) Use constraint decoding when decoding [13][14][15]. Although these works have made great contributions to machine translation in the field of low resources, there are still areas for improvement.…”

“…Hu et al [21]: A method of fusing key information by adding additional encoders. We follow the method of Hu et al and integrate the terms in the term dictionary as key information into the model by adding an encoder.…”

Neural Machine Translation of Electrical Engineering Based on Integrated Convolutional Neural Networks

“…In order to further demonstrate the effectiveness of our approach, comparative experiments were conducted on an electrical engineering dataset with our model and baseline models such as Sentence-level [24], Key Information Fusion [25], Pos Fusion [11], and Prior Knowledge [26]. The experimental conditions were kept consistent, and the results are shown in Table 8.…”

Neural Machine Translation of Electrical Engineering with Fusion of Memory Information

“…To further demonstrate the effectiveness of our method, we conducted comparative experiments with baseline models, vector fusion [26], and key information fusion [27] on a dataset in the field of electrical engineering. The experimental conditions were kept consistent, and the results are shown in Table 3 and the translation samples are shown in Table 4.…”