Semantic Composition via Probabilistic Model Theory

Functional Distributional Semantics provides a linguistically interpretable framework for distributional semantics, by representing the meaning of a word as a function (a binary classifier), instead of a vector. However, the large number of latent variables means that inference is computationally expensive, and training a model is therefore slow to converge. In this paper, I introduce the Pixie Autoencoder, which augments the generative model of Functional Distributional Semantics with a graphconvolutional neural network to perform amortised variational inference. This allows the model to be trained more effectively, achieving better results on two tasks (semantic similarity in context and semantic composition), and outperforming BERT, a large pre-trained language model.

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Semantic Composition via Probabilistic Model Theory

Cited by 8 publications

References 38 publications

Variational Inference for Logical Inference

Variational Inference for Logical Inference

Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus

Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics

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