Why would adding all the possible embeddings be "worse" than using 1D-convolutions?

Why Adding All Possible Embeddings Can Be Worse Than Using 1D Convolutions

In natural language processing (NLP) and many other sequence‑modeling tasks, two common strategies for extracting meaningful patterns from raw token sequences are embedding concatenation (sometimes called “adding all possible embeddings”) and 1‑dimensional convolutional neural networks (1D‑CNNs). While both aim to represent the input in a richer vector space, they differ drastically in efficiency, expressiveness, and generalization. Below we explore why the naive approach of stacking every conceivable embedding often turns out to be worse than letting a 1D‑CNN learn localized patterns.

1. Dimensionality Explosion

Embedding concatenation typically involves taking several pre‑trained vectors (e.g., word2vec, GloVe, FastText, BERT, ELMo) and concatenating them along the feature axis. If each embedding has d dimensions and you combine k of them, the resulting vector has k·d dimensions. For large k (or large d), this leads to:

• Memory blow‑up: GPU memory usage grows linearly with k·d. Training batches that comfortably fit with a single embedding may no longer fit.
• Parameter bloat: Subsequent dense layers must handle the inflated feature size, dramatically increasing the number of trainable weights and the risk of over‑fitting.

1D‑CNNs, on the other hand, keep the embedding dimension fixed (often a single vector per token) and learn a set of filters that slide across the sequence. The parameter count is proportional to filter_size × embedding_dim × num_filters, which is usually far smaller than the concatenated‑embedding approach.

2. Redundancy and Diminishing Returns

Many pre‑trained embeddings capture overlapping linguistic information. Concatenating them does not magically add new knowledge; instead, it injects a lot of redundant signals:

• Similar semantic subspaces are repeated, causing the model to waste capacity learning to ignore or down‑weight duplicate features.
• Redundant dimensions can interfere with gradient flow, making optimization harder.

A 1D‑CNN learns task‑specific filters that automatically combine whatever aspects of the embedding space are useful for the downstream objective. This targeted learning reduces redundancy because the network only keeps the patterns that improve the loss.

3. Lack of Local Pattern Modeling

Embedding concatenation treats each token independently, ignoring the crucial context of neighboring tokens. Word meaning often hinges on local word order (e.g., “not good” vs. “good”). 1D‑CNNs excel at capturing such n‑gram‑like patterns:

• Filters of width w act as learnable n‑grams, detecting phrases, prefixes, suffixes, and other sequential structures.
• Stacking multiple convolutional layers increases the receptive field, allowing the model to encode longer‑range dependencies without exploding dimensionality.

By only adding embeddings, you miss out on this built‑in capacity for local feature extraction, forcing later layers to recreate it from scratch—an inefficient and error‑prone process.

4. Generalization and Regularization

When the model is fed an enormous concatenated vector, it tends to memorize idiosyncrasies of the training data rather than learning robust patterns. 1D‑CNNs provide natural regularization:

• Weight sharing across time steps reduces the effective number of parameters.
• Pooling operations (max, average) further condense information, making the model less sensitive to noise.

These properties help the network generalize better to unseen sequences, whereas a high‑dimensional concatenated embedding can easily overfit.

5. Computational Efficiency

From a hardware perspective, convolutions are highly optimized on GPUs and specialized accelerators. Performing a single matrix multiplication on a massive concatenated vector is often slower and less parallelizable than a series of small, batched convolutions.

6. When Might Concatenation Help?

It’s not that concatenating embeddings is always a bad idea. In some low‑resource scenarios, fusing complementary pre‑trained embeddings can provide a modest boost—especially when the downstream model is shallow. However, the best practice is to:

• Project each embedding into a common low‑dimensional space (via a learned linear layer) before concatenation, or
• Use attention mechanisms to weight different embeddings dynamically.

Even then, a 1D‑CNN (or a Transformer) stacked on top usually yields superior performance because it still learns to exploit local structure.

Conclusion

Adding all possible embeddings creates a high‑dimensional, redundant, and context‑agnostic representation that strains memory, computation, and the model’s ability to generalize. 1D‑convolutions, by contrast, preserve a compact embedding size, enforce locality through learnable filters, share weights across positions, and are computationally efficient. For most sequence‑modeling tasks, especially those requiring nuanced pattern detection, 1D‑CNNs (or more advanced architectures that incorporate convolutions) are the smarter, more scalable choice.