Neural network models vs. MT evaluation metrics: a comparison between two approaches to automated assessment of information fidelity in consecutive interpreting

It is posits that the adoption of pre-trained neural network models has good potential as a viable method for automated assessment of information fidelity in interpreting and holds promise for broader applications in low-stake interpreting assessments and can supplement human scoring, particularly in large-scale assessment tasks.