An engineer in the early 1990s is developing a machine translation system. The system's underlying model, which predicts the likelihood of word sequences, is computationally limited and often fails to assign the highest probability to the best translation. The engineer must decide where to focus their efforts for the next six months. Which of the following two strategies would have been considered the more pragmatic and impactful approach during that era? Justify your reasoning.