In numerous tennis matches, people have discovered that there exists a kind of momentum in tennis competitions. When this kind of momentum forms, the player occupying the momentum exhibits an almost overwhelming consecutive win in this set. To study this kind of momentum, this paper uses the tennis match data of Jeff Sackmann on GitHub. Firstly, it conducts an exploratory analysis on the data of the men's singles tennis match at Wimbledon, and uses the TOPSIS comprehensive evaluation model with critical weighting to score the performance of the momentum of the players in a game, obtaining the broken-line graph of the momentum performance score for each point of both players. Afterwards, the random forest machine learning model with particle swarm hyperparameter optimization is used to fit and predict the change of momentum, and the confusion matrix indicates that the accuracy rate reaches 93.3%. The fitting results show that the number of times the player breaks serve and the cumulative number of game wins have outstanding contributions to the player's momentum.
With the rapid advancement of Neural Machine Translation (NMT), enhancing translation efficiency and quality has become a focal point of research. Despite the commendable performance of general models such as the Transformer in various aspects, they still fall short in processing long sentences and fully leveraging bidirectional contextual information. This paper introduces an improved model based on the Transformer, implementing an asynchronous and segmented bidirectional decoding strategy aimed at elevating translation efficiency and accuracy. Compared to traditional unidirectional translations from left-to-right or right-to-left, our method demonstrates heightened efficiency and improved translation quality, particularly in handling long sentences. Experimental results on the IWSLT2017 dataset confirm the effectiveness of our approach in accelerating translation and increasing accuracy, especially surpassing traditional unidirectional strategies in long sentence translation. Furthermore, this study analyzes the impact of sentence length on decoding outcomes and explores the model's performance in various scenarios. The findings of this research not only provide an effective encoding strategy for the NMT field but also pave new avenues and directions for future studies.
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