330
Concluding remarks and future work
The current prototype already successfully solves many cases of noun-phrase, verb-phrase,
prepositional-phrase, and adjectival-phrase translation (some actually better than the available
commercial systems), and contains a reasonable vocabulary for testing purposes, which
nevertheless still needs extending for real-world applications.
The following tasks have to be performed in order to have a working machine translation system:
Completing the coverage of structural transfer rules and monolingual and bilingual
vocabularies so that the system produces a translation for at least 90% of the English words and
performs the basic operations to identify and process correctly short constituents (1–6 words).
Releasing the resulting stable system as apertium-eng-kaz and disseminate it to the
interested parties to obtain feedback about its functioning. We can reasonably expect this system to
work better than the existing commercial systems in most aspects.
As a longer-range objective, and when a reasonably complete prototype is available, we will
tackle another interesting goal: the use of feedback from human input (for instance, in an interactive
machine translation system that provides completions to what the translator is typing).
Table 7: Example machine translation output for some simple phrases and examples.
Достарыңызбен бөлісу: