activity
20172020
most citedAssessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution

3 citations · 4 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2020

Character-level Transformer-based Neural Machine Translation

Nikolay Banar, Walter Daelemans, Mike Kestemont

Neural machine translation (NMT) is nowadays commonly applied at the subword level, using byte-pair encoding. A promising alternative approach focuses on character-level translatio…

cs.CV2020

On the Transferability of Winning Tickets in Non-Natural Image Datasets

Matthia Sabatelli, Mike Kestemont, Pierre Geurts

We study the generalization properties of pruned neural networks that are the winners of the lottery ticket hypothesis on datasets of natural images. We analyse their potential und…

cs.CL20191 cited

On the Feasibility of Automated Detection of Allusive Text Reuse

Enrique Manjavacas, Brian Long, Mike Kestemont

The detection of allusive text reuse is particularly challenging due to the sparse evidence on which allusive references rely---commonly based on none or very few shared words. Arg…

cs.CL2019

Improving Lemmatization of Non-Standard Languages with Joint Learning

Enrique Manjavacas, Ákos Kádár, Mike Kestemont

Lemmatization of standard languages is concerned with (i) abstracting over morphological differences and (ii) resolving token-lemma ambiguities of inflected words in order to map t…

cs.CL20173 cited

Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution

E. Manjavacas, J. de Gussem, W. Daelemans +1

Recent applications of neural language models have led to an increased interest in the automatic generation of natural language. However impressive, the evaluation of neurally gene…