11 citations · 23 across the 7 of their papers we have counts for
7 papers
Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP
Anya Belz, Craig Thomson, Ehud Reiter +39
We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/le…
Idioms, Probing and Dangerous Things: Towards Structural Probing for Idiomaticity in Vector Space
Filip Klubička, Vasudevan Nedumpozhimana, John D. Kelleher
The goal of this paper is to learn more about how idiomatic information is structurally encoded in embeddings, using a structural probing method. We repurpose an existing English v…
Probing Taxonomic and Thematic Embeddings for Taxonomic Information
Filip Klubička, John D. Kelleher
Modelling taxonomic and thematic relatedness is important for building AI with comprehensive natural language understanding. The goal of this paper is to learn more about how taxon…
Probing with Noise: Unpicking the Warp and Weft of Embeddings
Filip Klubička, John D. Kelleher
Improving our understanding of how information is encoded in vector space can yield valuable interpretability insights. Alongside vector dimensions, we argue that it is possible fo…
Domain-Specific Text Generation for Machine Translation
Yasmin Moslem, Rejwanul Haque, John D. Kelleher +1
Preservation of domain knowledge from the source to target is crucial in any translation workflow. It is common in the translation industry to receive highly specialized projects,…
Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study
Na Li, John D. Kelleher, Robert Ross
In order to enhance levels of engagement with conversational systems, our long term research goal seeks to monitor the confusion state of a user and adapt dialogue policies in resp…
Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines
Ammar N. Abbas, Georgios Chasparis, John D. Kelleher
An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes combining the advantages…