School of Foreign Languages, Fuzhou University of International Studies and Trade, Fuzhou, 350202, China.
Graduate School, University of Perpetual Help System DALTA, 1740, Las Pinas, Philippines.
Sci Rep. 2023 Mar 9;13(1):3948. doi: 10.1038/s41598-023-31053-5.
To accelerate the deep application of deep learning in text data processing, an English statistical translation system is established and applied to the question answering of humanoid robot. Firstly, the model of machine translation based on recursive neural network is implemented. A crawler system is established to collect English movie subtitle data. On this basis, an English subtitle translation system is designed. Then, combined with sentence embedding technology, the Particle Swarm Optimization (PSO) algorithm of meta-heuristic algorithm is adopted to locate the defects of translation software. A translation robot automatic question and answer interactive module is constructed. Additionally, the hybrid recommendation mechanism based on personalized learning is built using blockchain technology. Finally, the performance of translation model and software defect location model is evaluated. The results show that the Recurrent Neural Network (RNN) embedding algorithm has certain effect of word clustering. RNN embedded model has a strong ability to process short sentences. The strongest translated sentences are between 11 and 39 words long, while the weakest translated sentences are between 71 and 79 words long. Therefore, the model must strengthen the processing of long sentences, especially character-level input. The average sentence length is much longer than word-level input. The model based on PSO algorithm shows good accuracy in different data sets. This model averages better performance on Tomcat, standard widget toolkits, and Java development tool datasets than other comparison methods. The average reciprocal rank and average accuracy of the weight combination of PSO algorithm are very high. Moreover, this method is greatly affected by the dimension of the word embedding model, and the 300-dimension word embedding model has the best effect. To sum up, this study proposes a good statistical translation model for humanoid robot English translation, which lays the foundation for intelligent interaction between humanoid robots.
为了加速深度学习在文本数据处理中的深度应用，建立了一个英文统计翻译系统，并将其应用于仿人机器人的问答。首先，实现了基于递归神经网络的机器翻译模型。建立了一个爬虫系统来收集英语电影字幕数据。在此基础上，设计了一个英语字幕翻译系统。然后，结合句子嵌入技术，采用元启发式算法粒子群优化（PSO）算法对翻译软件的缺陷进行定位。构建了翻译机器人自动问答交互模块。此外，利用区块链技术构建了基于个性化学习的混合推荐机制。最后，对翻译模型和软件缺陷定位模型的性能进行评估。结果表明，递归神经网络（RNN）嵌入算法对单词聚类有一定的效果。RNN 嵌入模型对短句的处理能力较强。翻译效果最强的句子长度在 11 到 39 个单词之间，而翻译效果最弱的句子长度在 71 到 79 个单词之间。因此，模型必须加强对长句的处理，特别是字符级输入。基于 PSO 算法的模型在不同数据集上表现出良好的准确性。该模型在 Tomcat、标准小部件工具包和 Java 开发工具数据集上的平均性能优于其他比较方法。PSO 算法的权重组合的平均倒数排名和平均精度非常高。此外，该方法受词嵌入模型维度的影响较大，300 维词嵌入模型效果最佳。总之，本研究提出了一种用于仿人机器人英语翻译的良好统计翻译模型，为仿人机器人的智能交互奠定了基础。
