This paper introduces an attention-based encoder–decoder NMT architecture that learns soft alignments between source and target words while translating, eliminating the fixed-length bottleneck of earlier seq2seq models. The approach substantially improves BLEU, especially on long sentences, and matches phrase-based SMT on English-French without additional hand-engineered features. The attention mechanism it proposes became the foundation for virtually all subsequent NMT systems and inspired attention-centric models like the Transformer, reshaping machine translation and sequence modeling across NLP.
The paper “Attention Is All You Need” (2017) introduced the Transformer — a novel neural architecture relying solely on self-attention, removing recurrence and convolutions. It revolutionized machine translation by dramatically improving training speed and translation quality (e.g., achieving 28.4 BLEU on English-German tasks), setting new state-of-the-art benchmarks. Its modular, parallelizable design opened the door to large-scale pretraining and fine-tuning, ultimately laying the foundation for modern large language models like BERT and GPT. This paper reshaped the landscape of NLP and deep learning, making attention-based models the dominant paradigm across many tasks.
This paper introduces a Relational Memory Core that embeds multi-head dot-product attention into recurrent memory to enable explicit relational reasoning. Evaluated on synthetic distance-sorting, program execution, partially-observable reinforcement learning and large-scale language-modeling benchmarks, it consistently outperforms LSTM and memory-augmented baselines, setting state-of-the-art results on WikiText-103, Project Gutenberg and GigaWord. By letting memories interact rather than merely store information, the approach substantially boosts sequential relational reasoning and downstream task performance.
The BERT (Bidirectional Encoder Representations from Transformers) paper introduced a powerful pre-trained language model that uses deep bidirectional transformers and masked language modeling to capture both left and right context. Unlike prior unidirectional models, BERT achieved state-of-the-art performance across 11 NLP tasks (like GLUE, SQuAD) by enabling fine-tuning with minimal task-specific adjustments. Its impact reshaped NLP by setting a new standard for transfer learning, greatly improving accuracy on tasks such as question answering, sentiment analysis, and natural language inference, and inspiring a wave of follow-up models like RoBERTa, ALBERT, and T5.
