activity
20192021
most citedImproving Relation Extraction by Leveraging Knowledge Graph Link Prediction

3 citations · 6 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI20213 cited

Coarse-to-Fine Curriculum Learning

Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell +1

When faced with learning challenging new tasks, humans often follow sequences of steps that allow them to incrementally build up the necessary skills for performing these new tasks…

cs.CL2021

Re-TACRED: Addressing Shortcomings of the TACRED Dataset

George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos

TACRED is one of the largest and most widely used sentence-level relation extraction datasets. Proposed models that are evaluated using this dataset consistently set new state-of-t…

cs.CL2021

StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer

Yiwei Lyu, Paul Pu Liang, Hai Pham +4

Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style…

cs.CL20203 cited

Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction

George Stoica, Emmanouil Antonios Platanios, Barnabás Póczos

Relation extraction (RE) aims to predict a relation between a subject and an object in a sentence, while knowledge graph link prediction (KGLP) aims to predict a set of objects, O,…

cs.CV2019

LucidDream: Controlled Temporally-Consistent DeepDream on Videos

Joel Ruben Antony Moniz, Eunsu Kang, Barnabás Póczos

In this work, we aim to propose a set of techniques to improve the controllability and aesthetic appeal when DeepDream, which uses a pre-trained neural network to modify images by…