Search

With the development of natural language processing and neural machine translation, the neural machine translation method of end-to-end (E2E) neural network model has gradually become the focus of research because of its high translation accuracy and strong semantics of translation. However, there are still problems such as limited vocabulary and low translation loyalty, etc. In this paper, the discriminant method and the Conditional Random Field (CRF) model were used to segment and label the stem and affixes of Mongolian in the preprocessing stage of Mongolian-Chinese bilingual corpus. Aiming at the low translation loyalty problem, a decoding model combining Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) was constructed. The target language decoding was performed by using the GRU. A global attention model was used to obtain the bilingual word alignment information in the process of bilingual word alignment processing. Finally, the quality of the translation was evaluated by Bilingual Evaluation Understudy (BLEU) values and Perplexity (PPL) values. The improved model yields a BLEU value of 25.13 and a PPL value of − 3 8. 1. The experimental results show that the E2E Mongolian-Chinese neural machine translation model was improved in terms of translation quality and semantic confusion compared with traditional statistical methods and machine translation models based on Recurrent Neural Networks (RNN). [ABSTRACT FROM AUTHOR]

The neural machine translation model based on long short-term memory (LSTM) has become the mainstream in machine translation with its unique coding–decoding structure and semantic mining features. However, there are few studies on the Mongolian and Chinese neural machine translation combined with LSTM. This paper mainly studies the preprocessing of Mongolian and Chinese bilingual corpus and the construction of the LSTM model of Mongolian morpheme coding. In the corpus preprocessing stage, this paper presents a hybrid algorithm for the construction of word segmentation modules. The sequence that has not been annotated is treated semantically and labeled by a combination of gated recurrent unit and conditional random field. In order to learn more grammar and semantic knowledge from Mongolian corpus, in the model construction stage, this paper presents the LSTM neural network model based on morpheme coding to construct the encoder. This paper also constructs the LSTM neural network decoder to predict the Chinese decode. Experimental comparisons of sentences of different lengths according to the construction model show that the model has improved translation performance in dealing with long-term dependence problems. [ABSTRACT FROM AUTHOR]

The quality of meteorological observation data directly affects the weather forecast and the accuracy of climate prediction. The traditional quality control algorithm is not sensitive to the abnormal changes of the elements and can’t meet the needs of the quality control work. Therefore, based on the data mining algorithm, this paper further studied the quality control of meteorological data from two aspects of time correlation and factor correlation. Two different methods of quality control for meteorological observation data were proposed. One is the quality control method of time correlated meteorological observations based on the characteristics of chaos (potential trend and regularity) and the support vector machine algorithm. The other is the quality control method of factor correlated meteorological observations based on BP neural network and the characteristics of different elements. Combining the complementarity and relevance between the two methods, a set of comprehensive quality control scheme is set up. The experimental results show that the proposed scheme can effectively simulate the weather observation data and detect the anomaly value. [ABSTRACT FROM AUTHOR]