Order Matters: Sequence to Sequence for Sets

Abstract

The sequence-to-sequence framework is extended to handle input and output sets, and the importance of ordering in learning is demonstrated.

Introduction

Deep learning models, particularly RNNs and LSTMs, have achieved state-of-the-art results on sequential tasks, but challenges arise when dealing with unordered input or output data.

Related Work

Sequence-to-sequence models have been applied to various tasks beyond machine translation, with recent advancements incorporating external memories and attention mechanisms.

Neural Networks for Sequences and Sets

The paper explores how to extend the sequence-to-sequence framework to handle unordered input and output sets, highlighting the impact of data ordering on model performance.

Input Sets

Attention mechanisms are utilized to effectively integrate information from variable-length input sets, and empirical evidence shows that input order significantly affects sequence-to-sequence model performance.

Attention Mechanisms

A Read-Process-and-Write model using attention mechanisms is proposed to naturally handle input sets by creating a permutation-invariant embedding.

Sorting Experiment

An experiment on sorting numbers demonstrates that the proposed Read-Process-and-Write model outperforms the vanilla sequence-to-sequence approach, especially with processing steps and glimpses.

Output Sets

The paper investigates the impact of output ordering on sequence-to-sequence models, showing that while the chain rule theoretically handles any order, practical performance varies significantly.

Output Order Matters

Experiments on language modeling, parsing, combinatorial problems, and graphical models demonstrate that output ordering significantly affects the performance and convergence of sequence-to-sequence models.

Finding Optimal Orderings While Training

An efficient training algorithm is proposed that allows the model to learn the optimal ordering for applying the chain rule during training and inference.

5-Gram Modeling

The proposed framework successfully finds good orderings for 5-gram language modeling without prior knowledge, achieving comparable perplexity to models trained with known optimal orderings.

Conclusion

The paper successfully extends sequence-to-sequence models to handle unordered input and output sets and demonstrates the critical role of data ordering in achieving optimal performance.

References

This section lists related publications in machine learning and neural networks, including sequence-to-sequence learning, pointer networks, image captioning, memory networks, and reinforcement learning.