Seq2Seq: The Paper That Never Goes Out of Style

The Prelude: A Decade of Impact and the NeurIPS Test of Time Award

Among the buzz of new research, one announcement at the NeurIPS 2024 stole the spotlight—the Test of Time Paper Awards. This prestigious accolade recognizes research papers published a decade ago that have fundamentally shaped the field of machine learning, standing resilient against the relentless churn of innovation. One of the papers that received this award was: "Sequence to Sequence Learning with Neural Networks" by Ilya Sutskever, Oriol Vinyals, and Quoc Le.

This 2014 NeurIPS paper, now cited over 27,000 times, has been transformative. It introduced the world to the encoder-decoder architecture, a cornerstone of modern AI that has since evolved into attention mechanisms, transformers, and large language models (think ChatGPT or GPT-4). With all the buzz around foundation models and game-changing AI breakthroughs, it’s easy to forget the building blocks that got us here. Research like this set the stage for the cool stuff we see today. I actually shared this in my college tech group, and the discussion was so fun and engaging that I thought, why not turn it into a blog?

To understand why this paper matters so much, we’ll take a deep dive into the what, why, and how of sequence-to-sequence (seq2seq) learning. Why did we need it? What exactly does it do? And why is it considered the prequel to Transformers, laying the groundwork for the revolutionary shift we now take for granted?

But first, let’s pause and appreciate the brilliance of Ilya Sutskever—a name every machine learning enthusiast should know. Currently co-founder and Chief Scientist at OpenAI, Ilya is a visionary whose fingerprints are on some of the most transformative works in AI, from this seminal paper to the creation of GPT models. His ability to identify paradigm-defining problems and solutions has redefined what’s possible with machine learning. For anyone stepping into this field, learning about Sutskever’s contributions isn’t just an academic exercise—it’s an essential part of understanding how the AI landscape we see today came to life.

We’ll now embark on the journey to explore the foundations of seq2seq learning. Much like Taylor, seq2seq didn’t just break records—it reinvented the game entirely.

Setting the Stage: What Came Before Sequence-to-Sequence

It’s 2014. Taylor Swift is switching from her country roots to a full-blown pop genre with 1989. Meanwhile, in the world of AI, researchers are also transforming, trying to break free from the rigid systems of the past to create models that can handle the dynamic, unpredictable nature of sequential data. Researchers were grappling with a universal challenge: making machines understand sequences; be it language, music, or time-series data, in a way that adapts to context and complexity.

But back then, the tools were limited. There were no TensorFlow or PyTorch frameworks to ease the development process, and the hardware wasn’t as powerful as we enjoy today. Researchers had to build custom solutions for each problem. Let’s break this down.

Rule-Based Systems and Statistical Models

Before deep learning, sequential tasks like translation or speech recognition relied on methods such as:

• Hidden Markov Models (HMMs): Good at tasks like part-of-speech tagging but bad at understanding long-term dependencies, much like trying to guess the chorus of a song by only hearing the first two words of each verse.
• N-gram Language Models: Predict the next word based on a fixed “window” of previous words. Effective in the short term but terrible at capturing relationships across long sentences.

These methods worked in narrow use cases but couldn’t scale to the intricacies of real-world problems, such as translating a paragraph or understanding the nuance in a conversation.

The Research Gaps: Why We Needed a “New Era”

1. Fixed Input and Output Sizes: Models of the time could only handle sequences of a predefined length. This limitation made them impractical for translating languages, where sentences don’t follow strict word counts.
2. No End-to-End Learning: Sequential tasks often required stitching together multiple components, such as pre-processing, alignment, and generation, like having separate songwriters for each part of an album rather than a cohesive artist vision.
3. Task-Specific Models: The norm was to build models tailored to specific tasks, like translation or sentiment analysis. What was missing was a generalizable framework that could handle various sequential tasks, from subtitles for music videos to predictive text.

Dumbing Down DNNs

Deep Neural Networks (DNNs) are like the rockstars of machine learning. They’ve been headlining major gigs (or tasks) like speech recognition and image recognition. Why are they so good? Think of them as musicians who can play multiple instruments (or computations) in parallel, even with a limited band size (hidden layers). But, just like a concert needs a solid setlist (training data), DNNs thrive when there’s enough labeled data to help them learn.

However, even rockstars have their limitations. DNNs work best with inputs and outputs that have fixed dimensions—kind of like needing a perfectly tuned guitar to play a specific melody. But many real-world problems, involve sequences with varying lengths.

The Sequence-to-Sequence Breakthrough

The seq2seq paper was a pivotal moment for AI. It introduced a model that could process sequences of arbitrary lengths, generalize across tasks, and be trained end-to-end. Using an encoder-decoder architecture, the model solved two critical problems:

• The encoder converts the input sequence (e.g., a sentence in English) into a fixed-size context vector.?
• The decoder then generates the output sequence (e.g., the same sentence in French) step by step, conditioned on the encoded context.

P.S : We talk about French here, since the original paper talked about experiments conducted in French.?

In other words,

This revolutionary approach offered a general solution to sequential learning problems—no handcrafted features, no task-specific engineering—just input, output, and learning from data.

Breaking Down the Seq2Seq Architecture

You know that feeling when Taylor Swift sings "I cry a lot, but I'm so productive, it's an art," and you feel seen?? But my grandma, who only speaks Tamil, misses out.? Here’s where Seq2Seq comes into play. It acts like a translator: it listens to Taylor’s lyrics (input sequence), processes the meaning, and outputs the same song in Tamil (output sequence), preserving the emotion and rhythm.

Let’s dive into how it works under the hood.

1. Encoder: The Note-Taker

The encoder processes the input sequence (like the lyrics of a song) step by step, condensing all the information into a single “memory” vector. This vector is what the model uses to understand the essence of the input.

• Input: A sequence of words, say, ["this", "is", "me", "trying"].
• Output: A compressed vector representing the entire sequence.

This is done using recurrent neural networks (RNNs) or advanced versions like LSTMs or GRUs, which excel at handling sequential data.

2. The Bottleneck: Fixed-Size Memory Vector

Here’s where it gets tricky. Imagine you’ve summarized an entire album into one paragraph. That’s what the bottleneck does—it condenses the input sequence into a fixed-size vector. While this compression can work well for short sequences, it struggles with long ones (This has been solved later with attention, check out the Transformers: 10 Minute Version, if you want to read up on Attention.)

3. Decoder: The Translator

The decoder takes the memory vector from the encoder and “translates” it step by step into the desired output sequence. The decoder might output the same song in Tamil if the input was English lyrics.

It works like this:

• The decoder starts with a special token, usually <START>.
• It predicts the next word in the sequence using the memory vector and the previous output.
• Input: Memory vector + <START> token.
• Output: ["???", "????", "???????????????"].

4. Putting It All Together: Seq2Seq Pipeline

Here’s the full process:

1. The encoder processes the input sequence and generates a memory vector.
2. The decoder uses this memory vector to generate the output sequence one step at a time.
3. The final output is the translated sequence.

If we were to train a sample seq2seq model, the code structure would look like this:

import torch
import torch.nn as nn

# Simplified Seq2Seq Model for Illustration
class Seq2Seq(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(Seq2Seq, self).__init__()
        self.encoder = nn.LSTM(input_dim, hidden_dim, batch_first=True)
        self.decoder = nn.LSTM(hidden_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        _, (hidden, cell) = self.encoder(x)
        output, _ = self.decoder(hidden)
        return self.fc(output)

# Sample Data
input_dim, hidden_dim, output_dim = 128, 256, 128
seq2seq = Seq2Seq(input_dim, hidden_dim, output_dim)
data = torch.randn(32, 10, input_dim)  # Batch of 32, sequence length 10, feature size 128

# Forward Pass
output = seq2seq(data)
print("Output Shape:", output.shape)  # Expect (32, 10, 128)        

How do LSTMs work, and why do they shine in Seq2Seq tasks?

Let’s talk about Long Short-Term Memory networks (LSTMs)—the OGs of sequence modeling. They made it possible to model long-range dependencies. Without LSTMs, we’d be stuck in the shallow, surface-level modeling of relationships, like a one-hit wonder pop song from 2010.

Before LSTMs came along, traditional neural networks (even vanilla RNNs) had a glaring flaw: they forgot context. Imagine listening to The Archer and ignoring the first verse by the time you get to the bridge.?

That’s how standard RNNs handled sequential data—they struggled to remember earlier inputs when sequences were long.

The main antagonist here is the vanishing gradient problem. RNNs are like a game of Chinese Whispers, where the message fades over time; they can't maintain long-term relevance.? Similarly, gradients shrink exponentially as we backpropagate through time. LSTMs came in to fix this by creating a mechanism to preserve the “message” throughout the sequence.

Enter LSTMs

Think of RNNs (Recurrent Neural Networks) as your brain when you're reading a sentence - you process words one after another, using what you learned from previous words to understand the current one. Pretty neat, right?

But there's a catch - regular RNNs struggle with long sequences, kind of like trying to remember the beginning of a really long story by the time you get to the end. That's where LSTMs (Long Short-Term Memory) come in to save the day!

The paper discusses using LSTMs to predict what comes next in a sequence. Imagine you're playing a word prediction game - given "At teatime, everybody ___ .", you'd probably guess "agrees" or "sips". LSTMs do something similar, but with complex mathematical machinery under the hood.

Here's the key math from this RNN/LSTM section:

where:

• ht is the hidden state at time t
• xt is the input at time t
• yt is the output
• W terms are weight matrices
• sigm is the sigmoid activation function

For the LSTM part, they're estimating a conditional probability p(y1,...,yT' | x1,...,xT) where:

• x1...xT is your input sequence
• y1...yT' is your output sequence
• T and T' can be different lengths

The LSTM calculates this probability by:

1. Creating a representation v of the input sequence
2. Using v as the initial hidden state
3. Computing each probability step with standard LSTM equations

The overall probability is then:

In simpler terms: they're using a trained LSTM to predict each element of the output sequence one at a time, using all previous outputs as context. The final probability is just multiplying all these individual predictions together.

The authors tweaked the standard LSTM in three cool ways:

1. They used two separate LSTMs - one for input, one for output
2. They trained it on multiple languages simultaneously
3. They did some fancy optimization with deep networks

The goal? To make a system that can better understand and process sequences of words, especially when dealing with multiple languages.

What makes this particularly interesting is how they're pushing LSTMs beyond their typical limits to handle more complex language tasks. It's like giving your standard calculator superpowers!

"Think of an LSTM like a really good writer. They don't just throw everything they've ever learned into every story. They have 3 main components that make up its architecture, along with the Cell State (Long Term Memory) and Hidden State (Short Term Memory):

• The Forget Gate is like them deciding which past experiences to keep and which to leave behind – you know, the stuff that's not really relevant to the story they're trying to tell.
• The Input Gate is like them carefully researching and absorbing new ideas and inspiration to add depth to their writing.
• The Output Gate is like choosing the perfect words and phrases to bring their story to life, making sure every word counts."

Together, these gates enable the LSTM to remember long-term dependencies while processing immediate inputs.

Why LSTMs Were Revolutionary

LSTMs changed the game for sequence modeling by allowing models to:

• Handle long-range dependencies without forgetting.
• Avoid the vanishing gradient problem.
• Perform better in real-world tasks like language translation, speech recognition, and even text generation (hello, GPT).

Much like Taylor's evolving storytelling with albums like Folklore and Evermore, LSTMs brought depth, maturity, and consistency to sequence modeling.

Results :?

In this part of the paper, the authors evaluated how well their translation models performed using the cased BLEU score. For those new to BLEU, it’s a metric that measures how closely a machine-generated translation matches professional human translations. They used a tool called multi-bleu.pl to calculate these scores, which aligns with previous studies in this area and successfully replicates results from a baseline system.

To provide some context, the BLEU score of the best WMT'14 system, considered the gold standard in this space, stands at 37.0. The authors’ approach reached a BLEU score of 34.50 using an ensemble of LSTMs, falling just shy of this benchmark but still outperforming traditional phrase-based machine translation models.

Breaking Down Tables 1 and 2

Table 1: Comparing Different Methods

This table evaluates various methods on how well they translate English to French. Here’s the gist of the results:

1. The Baseline System achieved a BLEU score of 33.30, the benchmark.
2. A Single Forward LSTM, which translates without much "backup," scored 26.17—a respectable attempt but still far from the top.
3. Ensemble models, where multiple LSTMs collaborate (similar to the idea of many heads being better than one), achieved the highest scores. For instance, an ensemble of five reversed LSTMs with a beam size of 2 hit 34.50, overtaking the baseline.

Key takeaway: Ensembles of LSTMs consistently outperformed standalone models, showing that collaboration—even in AI—is a winning strategy.

Table 2: Enhancing with Rescoring

The second table focuses on methods that combine neural networks with traditional Statistical Machine Translation (SMT) systems. Think of it as taking the best of both worlds:

• Rescoring the baseline’s 1000-best translation candidates using a single LSTM brought the BLEU score to 35.61, an improvement over the baseline system.
• When an ensemble of five LSTMs was used for rescoring, the score rose to 36.5, closing the gap to the WMT'14 gold standard.

The highlight of Table 2 is the Oracle Rescoring, which achieves an estimated BLEU score of ~45. This result represents an ideal scenario where the system always selects the best possible candidate translation—a theoretical upper limit.

What Does This Mean?

The results underline two key points:

1. Neural networks are powerful but not invincible. While LSTMs significantly improve over traditional methods, they struggle with out-of-vocabulary words and other edge cases.
2. Collaboration wins. Like teamwork makes the dream work, ensemble models outperform individual ones, proving that a collective effort leads to better translations.

In the broader context of machine translation, this paper shows that while pure neural systems are closing in on traditional SMT approaches, hybrid methods still have room to shine.

Model Analysis and Graph Interpretations:

In this section, the paper delves into the learned representations of the LSTM model by visualizing its hidden states using a 2D PCA projection. This visualization sheds light on how the model processes phrases and assigns them vector representations in a fixed-dimensional space. One of the standout findings is how the LSTM’s representations are sensitive to word order—capturing semantic differences between similar phrases—while being relatively invariant to other syntactic alterations.

What is PCA?

Principal Component Analysis (PCA) is a dimensionality reduction technique that simplifies high-dimensional data into fewer dimensions (in this case, two), while retaining as much of the variation in the data as possible. By projecting the LSTM's hidden states into a 2D space, the clusters in the figure reveal how the model learns and groups semantically similar phrases.

The PCA projections showcase clusters intuitively grouped by meaning rather than mere surface-level word similarity. For instance:

• Phrases like "John admires Mary" and "Mary admires John" are semantically distinct, but the LSTM groups them close together based on underlying thematic relations.
• This behavior reflects how the LSTM learns structured relationships among tokens, unlike bag-of-words models, which lack sensitivity to word order.

Performance on Long Sentences:

The analysis extends to assessing the LSTM's performance on longer sentences, critical for real-world translation tasks. Figure 3 (right plot) shows:

• A consistent BLEU score trend across sentence lengths, demonstrates the LSTM's resilience.
• Interestingly, sentences with lengths exceeding 35 words show only minor degradation in translation quality, highlighting the model’s robust handling of long-term dependencies—an area where conventional baselines often struggle.

Quantitative and Qualitative Evidence:

Table 3 juxtaposes the LSTM translations against ground-truth sentences. Observations include:

1. The LSTM captures the core semantics of the sentence even when minor details (e.g., synonyms or restructuring) deviate from the ground truth.
2. The translations remain sensible and contextually accurate despite occasional errors (e.g., active-to-passive transformations). These errors underscore the challenge of capturing intricate grammar rules in sequence-to-sequence learning.

Industry Use Case: The Rise of Sequence-to-Sequence Learning

Before discussing the industry use cases, let’s first look at how sequence-to-sequence learning evolved in the neural network space. Think of the shift from Taylor’s country roots to her pop era as an analogy to how RNNs were used in the early days. RNNs (Recurrent Neural Networks) were the go-to architecture for sequential data, designed to process input sequences and output predictions. While they were a step in the right direction, they had limitations—long-range dependencies in sequences often caused issues, as the model struggled to retain context over long sequences.

This was where the seq2seq architecture, developed by researchers in 2014, came in. Seq2seq models used two RNNs (an encoder and a decoder) to handle the input and output sequences, respectively, making it possible to translate long data sequences efficiently. This breakthrough allowed for more complex tasks—like machine translation and speech recognition—to flourish.

Applications: What Happens When the Sequence Gets Transformed

Sequence-to-sequence learning has revolutionized the way machines handle tasks that involve transforming one sequence into another, making waves across diverse industries. At its core, this framework shines in machine translation—converting text from one language to another with remarkable accuracy and nuance. Think Google Translate, which processes billions of translation requests daily, bridging communication gaps across the globe. Beyond translation, seq2seq models power conversational AI, enabling chatbots like OpenAI’s ChatGPT and virtual assistants to understand context and craft coherent, human-like responses.

They’ve also been a game-changer in speech recognition, converting audio inputs into textual transcriptions at scale, as seen in tools like Apple's Siri and real-time captioning services. In healthcare, these models drive advancements like summarizing patient records, predicting medical events, and even enabling automated clinical documentation. The sheer scale of applications—from personalizing customer experiences in e-commerce to generating subtitles for global entertainment, demonstrates not only the versatility of seq2seq models but also their transformative impact on both everyday and mission-critical processes.

Conclusion

In this work, they demonstrated that a large deep LSTM, despite having a constrained vocabulary and making minimal assumptions about the problem structure, can surpass a standard SMT-based system with an unlimited vocabulary on a large-scale machine translation task. The success of this straightforward LSTM-based approach in translation suggests its potential for excelling in other sequence-to-sequence learning tasks, provided sufficient training data is available.

Reversing the words in source sentences surprisingly improved translation, highlighting the value of encoding strategies that simplify short-term dependencies. Contrary to expectations, LSTMs effectively translated long sentences when trained on reversed datasets, setting the stage for the Transformers revolution.

AND that, was the breakdown, of the seq2seq paper, folks! Hope you got to learn a thing or two :)

Things you should definitely check out:

• The original paper: https://paperswithcode.com/method/seq2seq
• Ilya's speech at NeurIPS 2024 : https://youtu.be/1yvBqasHLZs?si=a_Vb_1dcH5jP4MpR
• Implementing seq2seq in Keras: https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html
• PyTorch Implementation of seq2seq : https://github.com/bentrevett/pytorch-seq2seq/blob/main/1%20-%20Sequence%20to%20Sequence%20Learning%20with%20Neural%20Networks.ipynb