6 citations · 12 across the 11 of their papers we have counts for
14 papers
On the Complexity of the Compatibility Problem for Succinctly Encoded Conditional Distributions
Guy Emerson
The motivation for this paper is the investigation of the trade-offs implicit in probabilistic models used in machine learning. Models are often used to make predictions in the for…
Using dependency parsing for few-shot learning in distributional semantics
Stefania Preda, Guy Emerson
In this work, we explore the novel idea of employing dependency parsing information in the context of few-shot learning, the task of learning the meaning of a rare word based on a…
Visual Spatial Reasoning
Fangyu Liu, Guy Emerson, Nigel Collier
Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and…
Learning Functional Distributional Semantics with Visual Data
Yinhong Liu, Guy Emerson
Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability. It models the meaning of a wor…
Incremental Beam Manipulation for Natural Language Generation
James Hargreaves, Andreas Vlachos, Guy Emerson
The performance of natural language generation systems has improved substantially with modern neural networks. At test time they typically employ beam search to avoid locally optim…
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
Jun Yen Leung, Guy Emerson, Ryan Cotterell
Across languages, multiple consecutive adjectives modifying a noun (e.g. "the big red dog") follow certain unmarked ordering rules. While explanatory accounts have been put forward…
Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics
Guy Emerson
Functional Distributional Semantics provides a computationally tractable framework for learning truth-conditional semantics from a corpus. Previous work in this framework has provi…
Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics
Guy Emerson
Functional Distributional Semantics provides a linguistically interpretable framework for distributional semantics, by representing the meaning of a word as a function (a binary cl…
What are the Goals of Distributional Semantics?
Guy Emerson
Distributional semantic models have become a mainstay in NLP, providing useful features for downstream tasks. However, assessing long-term progress requires explicit long-term goal…
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models
Jeroen Van Hautte, Guy Emerson, Marek Rei
Word embeddings are an essential component in a wide range of natural language processing applications. However, distributional semantic models are known to struggle when only a sm…
Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting
Sebastian Borgeaud, Guy Emerson
We propose a method for natural language generation, choosing the most representative output rather than the most likely output. By viewing the language generation process from the…
Semantic Composition via Probabilistic Model Theory
Guy Emerson, Ann Copestake
Semantic composition remains an open problem for vector space models of semantics. In this paper, we explain how the probabilistic graphical model used in the framework of Function…
Variational Inference for Logical Inference
Guy Emerson, Ann Copestake
Functional Distributional Semantics is a framework that aims to learn, from text, semantic representations which can be interpreted in terms of truth. Here we make two contribution…
Functional Distributional Semantics
Guy Emerson, Ann Copestake
Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic fram…