activity
20182023
most citedWhat is not where: the challenge of integrating spatial representations into deep learning architectures

7 citations · 14 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2023

Grandma Karl is 27 years old -- research agenda for pseudonymization of research data

Elena Volodina, Simon Dobnik, Therese Lindström Tiedemann +1

Accessibility of research data is critical for advances in many research fields, but textual data often cannot be shared due to the personal and sensitive information which it cont…

cs.IR2022★ 2 cited

Multi-task recommendation system for scientific papers with high-way networks

Aram Karimi, Simon Dobnik

Finding and selecting the most relevant scientific papers from a large number of papers written in a research community is one of the key challenges for researchers these days. As…

cs.CL2021

We went to look for meaning and all we got were these lousy representations: aspects of meaning representation for computational semantics

Simon Dobnik, Robin Cooper, Adam Ek +5

In this paper we examine different meaning representations that are commonly used in different natural language applications today and discuss their limits, both in terms of the as…

cs.HC2019★ 3 cited

Referring to the recently seen: reference and perceptual memory in situated dialog

John D. Kelleher, Simon Dobnik

From theoretical linguistic and cognitive perspectives, situated dialog systems are interesting as they provide ideal test-beds for investigating the interaction between language a…

cs.LG2018★ 7 cited

What is not where: the challenge of integrating spatial representations into deep learning architectures

John D. Kelleher, Simon Dobnik

This paper examines to what degree current deep learning architectures for image caption generation capture spatial language. On the basis of the evaluation of examples of generate…

cs.CL2018★ 2 cited

Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing

Simon Dobnik, John D. Kelleher

Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectiv…