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Parallel corpora, essential for training translation models, are scarce for many language pairs. Potential solutions include unsupervised learning techniques that do not require parallel data, or the use of pivot languages to create synthetic parallel corpora.

Capturing contextual nuances when mapping between languages is challenging due to idiomatic expressions and cultural references. Context-aware translation systems and incorporation of world knowledge can mitigate this issue.

Languages vary greatly in morphology, affecting tokenization and parsing. To address this, morphological analyzers and subword tokenization techniques like Byte Pair Encoding (BPE) can be applied.

Different meanings of words (polysemy) and identical spelling of different words (homonymy) across languages hinder cross-lingual understanding. Disambiguation methods and contextual word embeddings can help in resolving these ambiguities.

Different writing systems and orthographic variation complicate processing and alignment. Transliteration and script normalization can assist in handling these variations.

Cross-lingual NLP systems might struggle with specialized terminology of different domains. Adapting models to recognize and translate domain-specific vocabulary with techniques such as domain adaptation can address this.

Understanding culture-specific context and implicit non-verbal cues is a barrier in cross-lingual communication. Multimodal NLP and the inclusion of cultural databases can help overcome these challenges.

Many languages lack sufficient digital presence for effective NLP. Techniques like few-shot learning and transfer learning from high resource languages can alleviate this problem.

Cross-lingual word alignment can be difficult due to structural differences between languages. Techniques like attention mechanisms or alignment models can be used to improve performance.