Abstract
Interactive machine translation is a technology that uses machine translation and human–computer interaction to improve the translation efficiency between natural languages. Although the current interactive machine translation has achieved some success, there are still some weaknesses. Based on this, this paper introduces the IFDTA into the interactive translation (IT) system, discusses the interactive machine translation method based on word graph and the application of the IFDTA in the IT system, overcomes the weakness of the current interactive machine translation, so as to improve the ability of the IT system and reduce the cost of user translation; The experimental results show that the translation efficiency of the system implemented in this paper is significantly improved on different corpora. Taking Parliamentary records as an example, the average translation time of the decision tree algorithm is 78.34 s, which is 22.83% shorter than the traditional IT. The addition of phrase translation item table can provide translators with more references of word translation items, effectively reduce the translation time delayed due to unknown words in the translation process and improve the translation efficiency.
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References
Nancy P, Muthurajkumar S, Ganapathy S et al (2020) Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks. IET Commun 14(5):888–895
Bakhshipour A, Zareiforoush H, Bagheri I (2020) Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features. J Food Meas Charact 14(3):1402–1416
Teekaraman D, Sendhilkumar S, Mahalakshmi GS (2020) Semantic provenance based trustworthy users classification on book-based social network using fuzzy decision tree. Int J Uncertain Fuzziness Knowl-Based Syst 28(1):47–77
Bian F, Wang X (2020) School enterprise cooperation mechanism based on improved decision tree algorithm. J Intell Fuzzy Syst 40(13):1–11
John SN, Adewale AA, Ndujiuba CN et al (2019) A neuro-fuzzy model for intelligent last mile routing. Int J Civil Eng Technol 10(1):2341–2356
Randhawa P, Shanthagiri V, Kumar A (2020) Violent activity recognition by E-textile sensors based on machine learning methods. J Intell Fuzzy Syst 39(6):8115–8123
Elavarasan D, Durai R (2020) Reinforced XGBoost machine learning model for sustainable intelligent agrarian applications. J Intell Fuzzy Syst 39(5):7605–7620
Wumaier H, Gao J, Zhou J (2020) Short-term forecasting method for dynamic traffic flow based on stochastic forest algorithm. J Intell Fuzzy Syst 39(8):1–13
Dhanalakshmi R, Devi TS (2020) Adaptive cognitive intelligence in analyzing employee feedback using LSTM. J Intell Fuzzy Syst 39(6):8069–8078
Kumar M, Reddy MR (2021) A C4.5 decision tree algorithm with MRMR features selection based recommendation system for tourists. Psychology (Savannah, Ga.) 58(1):3640–3643
Dhanjal AS, Singh W (2022) An automatic machine translation system for multi-lingual speech to Indian sign language. Multimedia Tools Appl 81(3):4283–4321
Meikle G (2020) ScreenPlay: a topic-theory-inspired interactive system. Organised Sound 25(1):89–105
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Wang, J., Liu, W., Liu, H. (2023). Interactive Translation System of Intelligent Fuzzy Decision Tree Algorithm (IFDTA). In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City - Volume 1. BDCPS 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 167. Springer, Singapore. https://doi.org/10.1007/978-981-99-0880-6_16
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DOI: https://doi.org/10.1007/978-981-99-0880-6_16
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