Design of Automatic Error Correction System for English Translation based on Reinforcement Learning Algorithm

Abstract

The paper investigates the mix of support learning calculations for automatic blunder adjustment in English interpretation, expecting to further develop interpretation exactness and familiarity. Through trial and error with Deep Q-Learning, Policy Gradient, Entertainer Pundit, and Deep Deterministic Policy Gradient (DDPG) calculations, it shows the adequacy of support learning in improving interpretation quality. Results show that DDPG accomplishes the most elevated typical award of 0.96 and meets quicker contrasted with different calculations. Moreover, the examination of various prize designs uncovers that molded award fundamentally further develops interpretation exactness and familiarity, with specialists prepared with formed reward accomplishing 82.6% precision and a familiarity score of 0.88. Similar examinations with standard techniques affirm the predominance of the proposed approach, with support learning-based blunder adjustment frameworks outflanking rule-based heuristics and administered learning draws near. The mix of manufactured and genuine world datasets guarantees the power and speculation of the blunder adjustment framework. Generally speaking, this examination adds to propelling machine interpretation by offering an information-driven and versatile answer for further developing interpretation quality, with expected applications in cross-lingual correspondence and regular language handling.