Absolutely! This is a super common ask in NLP—especially when you’re working with text where the order of chunks (like sentences in your example) shouldn’t change the overall representation. Let’s break down the key architectures and approaches that do exactly this:

1. Bag-of-Sentence-Embeddings (BoSE) with Order-Agnostic Pooling #

This is the simplest and most widely used approach, perfect for quick implementations:

• First, convert each sentence in your document to a fixed-dimensional embedding using a pre-trained sentence encoder (like Sentence-BERT, which excels at capturing sentence-level semantics).
• Then, apply a permutation-invariant pooling operation to the collection of sentence embeddings. The most common options are:
  • Mean pooling: Average all sentence embeddings together (your two example documents would both result in (vec_dogs + vec_cats)/2, so identical points)
  • Max pooling: Take the maximum value across each dimension of the sentence embeddings
  • Sum pooling: Add all sentence embeddings element-wise
• The output vector is your document’s low-dimensional, order-invariant representation.

2. Set Transformer #

If you need to capture subtle interactions between sentences while keeping order invariance, Set Transformers are purpose-built for this:

• Unlike standard Transformers, they skip positional encoding entirely and use attention mechanisms that only care about the relationships between elements (sentences, in your case), not their input order.
• They include specialized components like "Induced Set Attention Blocks" that help aggregate information across the entire set of sentences without relying on sequence order.
• This approach is great when you want more than just a simple average—for example, if certain sentences in the document are more semantically important than others, the model can learn to weight them appropriately while ignoring their order.

3. Deep Sets (Permutation-Invariant Neural Networks) #

Deep Sets are a general framework for learning from unordered collections, and they work beautifully for document embeddings:

• The core idea is to apply a nonlinear transformation to each sentence embedding individually, then use a symmetric pooling operation (mean/max/sum) to combine them, and finally apply another nonlinear transformation to the pooled result.
• Mathematically, this is f(X) = g(mean(h(x_i) for x_i in X)), where h and g are neural networks. Since mean pooling is permutation-invariant, the entire function f ignores the order of x_i (your sentences).
• This is more flexible than simple pooling because the h and g layers can learn complex patterns across the collection of sentences.

4. Graph-Based Document Embeddings #

Treat your document as an unordered graph of sentences, and use Graph Neural Networks (GNNs) to generate the embedding:

• Create a graph where each node represents a sentence, and edges represent semantic similarity between sentences (you can calculate this using cosine similarity of sentence embeddings).
• Use a GNN like GCN or GAT to update each node’s representation based on its neighbors. Since graphs don’t have an inherent node order, the GNN’s output will be invariant to the original sentence order.
• Finally, pool all updated node embeddings (mean/max) to get your document’s low-dimensional representation.

Quick Practical Tips #

• For most use cases, start with BoSE + mean pooling using Sentence-BERT—it’s fast, easy to implement, and gives great baseline results.
• If you need better performance on tasks where sentence interactions matter (like document classification with dependent sentences), move to Set Transformers or Deep Sets.
• Always make sure your sentence encoder captures the meaning of individual sentences (Sentence-BERT is a great choice here) — the document-level invariance comes from how you combine these sentence embeddings, not from ignoring sentence-internal word order.

内容的提问来源于stack exchange，提问作者pete