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Found 66 papers, 9 papers with code
Formalising and Learning a Quantum Model of Concepts
Our approach builds upon Gardenfors' classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains.
The Conceptual VAE
In this report we present a new model of concepts, based on the framework of variational autoencoders, which is designed to have attractive properties such as factored conceptual domains, and at the same time be learnable from data.
lambeq: An Efficient High-Level Python Library for Quantum NLP
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP).
Something Old, Something New: Grammar-based CCG Parsing with Transformer Models
This report describes the parsing problem for Combinatory Categorial Grammar (CCG), showing how a combination of Transformer-based neural models and a symbolic CCG grammar can lead to substantial gains over existing approaches.
Formalising Concepts as Grounded Abstractions
The mathematics of partial orders and lattices is a standard tool for modelling conceptual spaces (Ch. 2, Mitchell (1997), Ganter and Obiedkov (2016)); however, there is no formal work that we are aware of which defines a conceptual lattice on top of a representation that is induced using unsupervised deep learning (Goodfellow et al., 2016).
Imitating Interactive Intelligence
These evaluations convincingly demonstrate that interactive training and auxiliary losses improve agent behaviour beyond what is achieved by supervised learning of actions alone.
Representation Learning for Type-Driven Composition
This paper is about learning word representations using grammatical type information.
Learning to Personalize for Web Search Sessions
In this paper, we formulate session search as a personalization task under the framework of learning to rank.
Grounded Language Learning Fast and Slow
Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning.
Probing Emergent Semantics in Predictive Agents via Question Answering
Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments.
Learning to Segment Actions from Observation and Narration
We apply a generative segmental model of task structure, guided by narration, to action segmentation in video.
Environmental drivers of systematicity and generalization in a situated agent
The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI.
Neural Generative Rhetorical Structure Parsing
We show that, for our parser's traversal order, previous beam search algorithms for RNNGs have a left-branching bias which is ill-suited for RST parsing.
Scalable Syntax-Aware Language Models Using Knowledge Distillation
Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential language models.
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Language exhibits hierarchical structure, but recent work using a subject-verb agreement diagnostic argued that state-of-the-art language models, LSTMs, fail to learn long-range syntax sensitive dependencies.
Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing
Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task.
Factorising AMR generation through syntax
Generating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not constrained by the semantic graph.
Emergent Communication through Negotiation
We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks.
Understanding Grounded Language Learning Agents
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds.
Understanding Early Word Learning in Situated Artificial Agents
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-person actors in partially-observable worlds.
Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain
Research in computational semantics is increasingly guided by our understanding of human semantic processing.
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs
It can therefore be seen as a tree-based RNN that is unsupervised with respect to the parse trees.
Modelling metaphor with attribute-based semantics
One of the key problems in computational metaphor modelling is finding the optimal level of abstraction of semantic representations, such that these are able to capture and generalise metaphorical mechanisms.
Latent Variable Dialogue Models and their Diversity
We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the `boring output' issue of deterministic dialogue models.
Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns
Dual coding theory considers concrete concepts to be encoded in the brain both linguistically and visually, and abstract concepts only linguistically.
Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research
Meaning has been called the "holy grail" of a variety of scientific disciplines, ranging from linguistics to philosophy, psychology and the neurosciences.
Using Sentence Plausibility to Learn the Semantics of Transitive Verbs
The functional approach to compositional distributional semantics considers transitive verbs to be linear maps that transform the distributional vectors representing nouns into a vector representing a sentence.
The Frobenius anatomy of word meanings II: possessive relative pronouns
Within the categorical compositional distributional model of meaning, we provide semantic interpretations for the subject and object roles of the possessive relative pronoun `whose'.
Evaluation of Simple Distributional Compositional Operations on Longer Texts
Distributional semantic models have been effective at representing linguistic semantics at the word level, and more recently research has moved on to the construction of distributional representations for larger segments of text.
The Frobenius anatomy of word meanings I: subject and object relative pronouns
This paper develops a compositional vector-based semantics of subject and object relative pronouns within a categorical framework.
A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing
Semantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation.
Learning Type-Driven Tensor-Based Meaning Representations
This paper investigates the learning of 3rd-order tensors representing the semantics of transitive verbs.
A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure
We discuss an algorithm which produces the meaning of a sentence given meanings of its words, and its resemblance to quantum teleportation.
Mathematical Foundations for a Compositional Distributional Model of Meaning
We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, for which we rely on the algebra of Pregroups, introduced by Lambek.
