Many important tasks in natural language processing and other domains involve transforming one sequence into another. We call these sequence-to-sequence (Seq2Seq) tasks. Think about machine translation: converting a sequence of words in one language (e.g., English) into a sequence of words in another (e.g., French). Other examples include:

Text Summarization: Mapping a long document (input sequence) to a shorter summary (output sequence).
Question Answering: Taking a context passage and a question (often concatenated or treated as sequences) and generating an answer sequence.
Speech Recognition: Converting an audio sequence into a text sequence.

Here's a conceptual view of a generic Seq2Seq task like translation:

A simple illustration of a sequence-to-sequence task, mapping an input sequence ("The", "cat", "sat") to an output sequence ("Le", "chat", "assis") via a model.

While the concept seems straightforward, effectively modeling these transformations presents several significant hurdles.

Representing Order and Context

The meaning of a sequence often depends critically on the order of its elements. "The cat chased the dog" means something entirely different from "The dog chased the cat". A successful model must not only understand the individual elements (words, in this case) but also how their position and the surrounding elements influence the overall meaning. It needs to capture the contextual relationships within the sequence.

Handling Long-Range Dependencies

One of the most persistent difficulties in sequence modeling is capturing long-range dependencies. This refers to situations where understanding or predicting an element in the sequence requires information from elements that appeared much earlier.

Consider this example:

"I grew up in a small village in the south of France, near the Pyrenees. Although I moved away many years ago, I still visit often. As a result, I speak fluent French."

To correctly predict "French" at the end, the model needs to connect it back to "France" mentioned several sentences earlier. If the intermediate text were much longer, this connection becomes even harder to maintain. Models need mechanisms to "remember" or access relevant information across potentially vast distances within the sequence, avoiding the dilution or loss of this information over time or position. Traditional approaches often struggle here, as their "memory" can be limited.

The Fixed-Context Bottleneck

Early approaches to Seq2Seq tasks often involved summarizing the entire input sequence into a single, fixed-size vector representation (often called a "context vector" or "thought vector"). This vector was then expected to contain all the necessary information from the input sequence for the model to start generating the output sequence.

Imagine trying to summarize this entire chapter into a single, short sentence. You'd inevitably lose a lot of detail and nuance. Similarly, forcing a complex input sequence, especially a long one, into one fixed-size vector creates an information bottleneck. It's difficult for the model to encode every important detail, leading to degraded performance, particularly on longer or more complex sequences. The model might forget earlier parts of the input or fail to capture subtle relationships.

Variable Input and Output Lengths

Seq2Seq tasks rarely involve inputs and outputs of the same length. A short phrase in one language might translate to a longer sentence in another. A lengthy article might be summarized into just a few sentences. The model architecture must be flexible enough to handle these variations, consuming an input of arbitrary length $N$ and generating an output of arbitrary length $M$ , where $N$ and $M$ can be different for each example.

These challenges highlight the need for architectures that can effectively capture sequential dependencies, handle long-range context without information loss, and manage variable sequence lengths. Understanding these difficulties motivates the development of mechanisms like attention, which directly address the bottleneck problem and improve the handling of dependencies, paving the way for models like the Transformer. In the next section, we'll briefly review Recurrent Neural Networks (RNNs), an earlier approach designed to handle sequential data, before examining their specific limitations.