Tsetlin Machine Embedding: Representing Words Using Logical Expressions

Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating similar words in vector space. For instance, Word2Vec is a self-supervised predictive model that captures the context of words using a neural network. Similarly, GLoVe is a popular unsupervised model incorporating corpus-wide word co-occurrence statistics. Such word embedding has significantly boosted important NLP tasks, including sentiment analysis, document classification, and machine translation. However, the embeddings are dense floating-point vectors, making them expensive to compute and difficult to interpret. In this paper, we instead propose to represent the semantics of words with a few defining words that are related using propositional logic. To produce such logical embeddings, we introduce a Tsetlin Machine-based autoencoder that learns logical clauses self-supervised. The clauses consist of contextual words like "black," "cup," and "hot" to define other words like "coffee," thus being human-understandable. We evaluate our embedding approach on several intrinsic and extrinsic benchmarks, outperforming GLoVe on six classification tasks. Furthermore, we investigate the interpretability of our embedding using the logical representations acquired during training. We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.

Human Interpretable AI: Enhancing Tsetlin Machine Stochasticity with Drop Clause

In this article, we introduce a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the key learning elements of a TM. In effect, TM with drop clause ignores a random selection of the clauses in each epoch, selected according to a predefined probability. In this way, additional stochasticity is introduced in the learning phase of TM. Along with producing more distinct and well-structured patterns that improve the performance, we also show that dropping clauses increases learning robustness. To explore the effects clause dropping has on accuracy, training time, and interpretability, we conduct extensive experiments on various benchmark datasets in natural language processing (NLP) (IMDb and SST2) as well as computer vision (MNIST and CIFAR10). In brief, we observe from +2% to +4% increase in accuracy and 2x to 4x faster learning. We further employ the Convolutional TM to document interpretable results on the CIFAR10 dataset. To the best of our knowledge, this is the first time an interpretable machine learning algorithm has been used to produce pixel-level human-interpretable results on CIFAR10. Also, unlike previous interpretable methods that focus on attention visualisation or gradient interpretability, we show that the TM is a more general interpretable method. That is, by producing rule-based propositional logic expressions that are \emph{human}-interpretable, the TM can explain how it classifies a particular instance at the pixel level for computer vision and at the word level for NLP.