Improving language modeling: Using techniques like n-gram models, recurrent neural networks (RNNs), and transformers to better understand the structure and context of ...
Improving language modeling: Using techniques like n-gram models, recurrent neural networks (RNNs), and transformers to better understand the structure and context of languages.
Leveraging parallel corpora: Utilizing large datasets of aligned text to train models that can accurately translate between languages.
Incorporating morphological and syntactic analysis: Breaking down words into their constituent parts and analyzing sentence structure to improve translation accuracy.
Using transfer learning: Applying knowledge gained from one language pair to improve translation for other language pairs.
Developing evaluation metrics: Creating metrics like BLEU and ROUGE to measure the quality of machine-translated text.
Leveraging parallel corpora: Utilizing large datasets of aligned text to train models that can accurately translate between languages.
Incorporating morphological and syntactic analysis: Breaking down words into their constituent parts and analyzing sentence structure to improve translation accuracy.
Using transfer learning: Applying knowledge gained from one language pair to improve translation for other language pairs.
Developing evaluation metrics: Creating metrics like BLEU and ROUGE to measure the quality of machine-translated text.