Translation is one of the hardest ML/NLP problem and it is far from being solved. AFAIK, current state of the art translation systems are not working with a la

Translation is one of the hardest ML/NLP problem and it is far from being solved. AFAIK, current state of the art translation systems are not working with a layered approach as you defined (morphology->syntax->semantics->interlingua?). First, they do not use interlingua. They use statistical models from example translations (translation model) and target language (language model). They started to include syntactic and semantic clues recently but it is mostly adding extra information to the models and alignment-search process. So computational complexity is not the real issue when it comes to translation quality. For most (if not all) languages, systems simply cannot achieve human translators level yet. For languages that lack large amount of example translation texts, machine generated translations are still terrible.