46 citations · 69 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Deep learning evaluation using deep linguistic processing
Alexander Kuhnle, Ann Copestake
We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement…
ShapeWorld - A new test methodology for multimodal language understanding
Alexander Kuhnle, Ann Copestake
We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities. In this approach, artificia…