Morphology generation for English-Indian language statistical machine translation

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

“…Figure C from element I in V to the actual vector (see Figure 2) represents the representation of each word in the word and its corresponding attribute. In fact, C represents a matrix of |V| × m. If G is used to represent the probability function of a word, function g represents the conditional probability distribution from the input order of the feature vector of the word in the context to the next word in word v. erefore, combining these two steps can obtain the result as shown in formulas ( 8) and (9).…”

“…Sreelekha et al said that to establish various information retrieval systems, it is necessary to automatically index documents and even use computers to process natural languages. It is necessary to solve the problem of automatic analysis of natural language texts, which is closely related to machine translation [ 9 ].…”

A Graph Auto-encoder Model of Derivational Morphology

“…In NLP, the lack of reliable ways to estimate the MWF of derivatives poses a bottleneck for generative models, particularly in languages exhibiting a rich derivational morphology; e.g., while inflected forms can be translated by generating morphologically corresponding forms in the target language (Minkov et al, 2007), generating derivatives is still a major challenge for machine translation systems (Sreelekha and Bhattacharyya, 2018). Similar problems exist in the area of automatic language generation (Gatt and Krahmer, 2018).…”