Week 6 study summary of Naver Boostcamp AI Tech covering NLP lectures, bucketing for data batching, and peer session discussions.

BLEU score for machine translation evaluation: precision, recall, F1, n-gram overlap, and brevity penalty.

Explanation of beam search decoding as a trade-off between greedy decoding and exhaustive search in sequence generation.

Explanation of the attention mechanism in Seq2Seq models, covering attention vectors, context vectors, teacher forcing, and similarity functions.

Detailed explanation of LSTM and GRU architectures, covering gate mechanisms, cell state, and how they solve RNN's long-term dependency problem.

Deep dive into RNN types (one-to-one, one-to-many, many-to-many), character-level language models, and LSTM/GRU architectures.