[2] Seq2Seq - Sequence to Sequence Learning with Neural Networks

Title: Sequence to Sequence Learning with Neural Networks

Authors & Year: Ilya Sutskever, Oriol Vinyals, and Quoc V. Le, 2014

Link: https://arxiv.org/abs/1409.3215

Objective: Develop a general-purpose approach to learn mappings from input sequences to output sequences using neural networks, with a focus on machine translation.

Context: Before sequence-to-sequence learning, NLP tasks involving input-output mappings, like machine translation, were addressed using complex pipelines and rule-based systems that were difficult to maintain and improve.

Key Contributions:

• Introduced the encoder-decoder architecture using recurrent neural networks (RNNs) for sequence-to-sequence learning.

• Demonstrated the effectiveness of the model on English-to-French translation tasks.

Methodology:

• The encoder reads the input sequence and encodes it into a fixed-length vector representation.

• The decoder takes the encoded vector and generates the output sequence, one token at a time.

Results:

• The sequence-to-sequence model achieved state-of-the-art performance on the WMT'14 English-to-French translation task.

• The model successfully learned long-range dependencies and complex linguistic structures.

Impact:

• Established the foundation for complex tasks like machine translation, text summarization, and dialogue systems.

• Inspired further research in NLP, including the development of attention mechanisms and transformer architectures.

Takeaways:

• The sequence-to-sequence model maps input sequences to output sequences using an encoder-decoder architecture with RNNs.

• The model proved effective in machine translation tasks, learning complex linguistic structures and dependencies.

• The encoder-decoder approach has had a significant impact on NLP research and the development of more advanced models.