We propose a novel approach to learning distributed representations of variable-length text sequences in multiple languages simultaneously. Unlike previous work which often derive representations of multi-word sequences as weighted sums of individual word vectors, our model learns distributed representations for phrases and sentences as a whole. Our work is similar in spirit to the recent parag...

Most statistical machine translation (SMT) systems are modeled using a loglinear framework. Although the log-linear model achieves success in SMT, it still suffers from some limitations: (1) the features are required to be linear with respect to the model itself; (2) features cannot be further interpreted to reach their potential. A neural network is a reasonable method to address these pitfall...

Recently, Acritical Intelligent (AI) methodologies such as Long and Short-term Memory (LSTM) have been widely considered promising tools for engine performance calibration, especially emission prediction optimization, Transformer is also gradually applied to sequence prediction. To carry out high-precision control predicting long time step sequences required. However, LSTM has the problem of gr...

t-stochastic neighbor embedding (t-SNE) is a nonparametric data visualization method in classical machine learning. It maps the from high-dimensional space into low-dimensional space, especially two-dimensional plane, while maintaining relationship or similarities between surrounding points. In t-SNE, initial position of randomly determined, and achieved by moving to minimize cost function. Its...

Due to the expansion of data generation, more and natural language processing (NLP) tasks are needing be solved. For this, word representation plays a vital role. Computation-based embedding in various high languages is very useful. However, until now, low-resource such as Bangla have had limited resources available terms models, toolkits, datasets. Considering this fact, paper, an enhanced Ban...