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Technical Paper: Sequence to Sequence Learning with Neural Networks, how did this help with Transformers

 The paper "Sequence to Sequence Learning with Neural Networks" is a landmark work by Ilya Sutskever, Oriol Vinyals, and Quoc V. Le, published in 2014. It introduced a general approach to handling sequence-to-sequence problems using neural networks, specifically using Recurrent Neural Networks (RNNs).

Summary of the Paper

Problem:

Traditional neural networks struggled with tasks where the input and output were sequences of different lengths (e.g., machine translation, speech recognition).

Solution:

The authors proposed a model with two main components:

  1. Encoder RNN:

    • Processes the input sequence (e.g., a sentence in English) one step at a time.
    • At each step, it updates its hidden state.
    • After the entire sequence is read, the final hidden state is a fixed-size vector representation of the input sequence.

  2. Decoder RNN:

    • Takes the fixed-size vector from the encoder as input.
    • Generates the output sequence (e.g., sentence in French) one token at a time.
    • The decoder also uses its own hidden states and previously generated outputs.

Key Innovations:

  • Handling Variable Length Input and Output: The encoder-decoder framework naturally handles sequences of varying lengths.

  • Reversing Input Sequences: The authors found that reversing the input sequence improves the optimization process, likely because it reduces the "distance" between corresponding input and output tokens.

  • Use of LSTMs (Long Short-Term Memory): They used LSTM cells in both encoder and decoder to handle long-range dependencies and vanishing gradient problems.

Applications:

  • Machine Translation (primary focus)
  • Speech Recognition
  • Text Summarization
  • Chatbots

Impact:

This paper laid the groundwork for many subsequent advancements in natural language processing (NLP) and sequence learning. It was foundational before the rise of Transformer-based models like BERT and GPT.

Link to PDF:

You can access the official paper here:

📄 Sequence to Sequence Learning with Neural Networks (PDF)

How did Sequence to Sequence Learning with Neural Networks, how did this help with Transformers.

The paper "Sequence to Sequence Learning with Neural Networks" (2014) played a crucial role in shaping the foundation upon which Transformers were later built. Let me explain how it contributed to the evolution towards Transformers:

1. Sequence-to-Sequence (Seq2Seq) Idea → Core Problem Definition

  • The Seq2Seq framework formalized the problem of mapping one sequence to another.
    • Example: Translating "Hello World" (English) → "Bonjour le monde" (French).

✅ Impact on Transformers:

  • Transformers also solve the same Seq2Seq problem but replace RNNs with a more efficient mechanism (attention).

2. Encoder-Decoder Architecture → Structural Inspiration

  • The paper introduced the Encoder-Decoder architecture:
    • Encoder: Compresses the input sequence into a fixed-size context vector.
    • Decoder: Generates the output sequence from that vector.

✅ Impact on Transformers:

  • Transformers kept the Encoder-Decoder design but improved the components:
    • Replacing RNNs with self-attention layers.
    • Allowing more parallelism and handling long-range dependencies better.

3. Bottleneck Problem → Motivation for Attention

  • Problem in Seq2Seq RNNs:
    • The entire input sequence is squeezed into one fixed-size vector (context vector).
    • This limits the model’s ability to handle long sequences (information bottleneck).

✅ Impact on Transformers:

  • This bottleneck led to the introduction of Attention Mechanisms:
    • Instead of relying solely on one vector, attention allows the decoder to directly access all encoder outputs at each step.
  • First seen in Bahdanau Attention (2015), directly influenced by the weaknesses of this Seq2Seq paper.
  • Transformers take attention further → they are built entirely on self-attention, no RNNs at all.

4. Handling Variable Length Sequences

  • Seq2Seq made it clear that:
    • Traditional neural networks can’t handle variable-length input/output easily.
    • Encoder-decoder solves it.

✅ Impact on Transformers:

  • Transformers also naturally handle variable-length sequences, continuing this capability but improving efficiency.

5. Long-Term Dependencies → Transformer’s Breakthrough

  • Even though LSTMs (used in Seq2Seq) help with long-term dependencies, they are sequential in nature → hard to parallelize and still struggle with very long sequences.

✅ Impact on Transformers:

  • Transformers removed recurrence entirely, using attention to model relationships regardless of distance.
  • Enabled parallel processing, making training faster.

Summary:

Seq2Seq with RNNs (2014) Transformers (2017)
Encoder-Decoder architecture Encoder-Decoder retained
Bottlenecked by fixed-size vector Attention over all inputs eliminates bottleneck
Uses RNNs (LSTMs) → sequential Uses self-attention → parallelizable
Handles variable-length sequences Handles variable-length sequences
Struggles with long-term dependencies Attention solves long dependencies efficiently

Direct Lineage:

  1. Seq2Seq Paper (2014) → defined the architecture.
  2. Attention Mechanism (Bahdanau et al., 2015) → solved bottlenecks.
  3. Transformer (Vaswani et al., 2017) → built fully on attention, no RNNs.


Link to Technical Paper: https://arxiv.org/pdf/1409.3215

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