3 citations · 7 across the 9 of their papers we have counts for
4 papers · 1 filter
Topic Aware Probing: From Sentence Length Prediction to Idiom Identification how reliant are Neural Language Models on Topic?
Vasudevan Nedumpozhimana, John D. Kelleher
Transformer-based Neural Language Models achieve state-of-the-art performance on various natural language processing tasks. However, an open question is the extent to which these m…
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…
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,…