On the Relationship Between RNN Hidden State Vectors and Semantic Ground Truth

TL;DR

This paper investigates whether RNN hidden states naturally form semantically meaningful clusters, using automata-based evaluation to validate the clustering hypothesis in modern neural networks.

Contribution

It provides a formal analysis and empirical evidence supporting the clustering hypothesis in RNNs trained on regular languages, using ground-truth automata for validation.

Findings

Hidden-state vectors are often separable into semantic classes.

Unsupervised clustering can effectively identify meaningful clusters.

The clustering hypothesis holds in most examined cases.

Abstract

We examine the assumption that the hidden-state vectors of recurrent neural networks (RNNs) tend to form clusters of semantically similar vectors, which we dub the clustering hypothesis. While this hypothesis has been assumed in the analysis of RNNs in recent years, its validity has not been studied thoroughly on modern neural network architectures. We examine the clustering hypothesis in the context of RNNs that were trained to recognize regular languages. This enables us to draw on perfect ground-truth automata in our evaluation, against which we can compare the RNN's accuracy and the distribution of the hidden-state vectors. We start with examining the (piecewise linear) separability of an RNN's hidden-state vectors into semantically different classes. We continue the analysis by computing clusters over the hidden-state vector space with multiple state-of-the-art unsupervised clustering approaches. We formally analyze the accuracy of computed clustering functions and the validity of the clustering hypothesis by determining whether clusters group semantically similar vectors to the same state in the ground-truth model. Our evaluation supports the validity of the clustering hypothesis in the majority of examined cases. We observed that the hidden-state vectors of well-trained RNNs are separable, and that the unsupervised clustering techniques succeed in finding clusters of similar state vectors.