The predominance of government and state-related sources in existing parallel corpora for Kazakh NLP can introduce several biases and limitations:
Political Bias: Government-related texts may reflect a particular political perspective or agenda, leading to biased translations that may not accurately represent diverse viewpoints.
Cultural Bias: State-related sources may not adequately capture the cultural nuances and diversity of language use in everyday contexts, potentially leading to cultural biases in translations.
Limited Vocabulary: Government texts may have a specific vocabulary and terminology that differs from colloquial language, limiting the model's ability to accurately translate informal or non-official content.
Lack of Diversity: Over-reliance on government sources can limit the diversity of topics and language styles in the corpus, hindering the model's ability to handle a wide range of translation tasks effectively.
Quality and Accuracy: Government texts may contain complex legal or technical language that can be challenging to translate accurately, potentially impacting the overall quality of the translations generated by the model.
Generalizability: Models trained on government-centric data may struggle to generalize to other domains or informal language use, affecting their performance on a broader range of translation tasks.