activity
20182022
most citedSEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression

22 citations · 65 across the 11 of their papers we have counts for

collaborators

19 papers

cs.CL2022

Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis

Odysseas S. Chlapanis, Georgios Paraskevopoulos, Alexandros Potamianos

Multimodal learning pipelines have benefited from the success of pretrained language models. However, this comes at the cost of increased model parameters. In this work, we propose…

cs.SD20221 cited

A Dataset for Greek Traditional and Folk Music: Lyra

Charilaos Papaioannou, Ioannis Valiantzas, Theodoros Giannakopoulos +2

Studying under-represented music traditions under the MIR scope is crucial, not only for developing novel analysis tools, but also for unveiling musical functions that might prove…

cs.CV20222 cited

Extending Compositional Attention Networks for Social Reasoning in Videos

Christina Sartzetaki, Georgios Paraskevopoulos, Alexandros Potamianos

We propose a novel deep architecture for the task of reasoning about social interactions in videos. We leverage the multi-step reasoning capabilities of Compositional Attention Net…

cs.LG20222 cited

MMLatch: Bottom-up Top-down Fusion for Multimodal Sentiment Analysis

Georgios Paraskevopoulos, Efthymios Georgiou, Alexandros Potamianos

Current deep learning approaches for multimodal fusion rely on bottom-up fusion of high and mid-level latent modality representations (late/mid fusion) or low level sensory inputs…

cs.CL20211 cited

EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments

Emmanouil Zaranis, Georgios Paraskevopoulos, Athanasios Katsamanis +1

In this paper, we introduce EmpBot: an end-to-end empathetic chatbot. Empathetic conversational agents should not only understand what is being discussed, but also acknowledge the…

cs.CL2021

UDALM: Unsupervised Domain Adaptation through Language Modeling

Constantinos Karouzos, Georgios Paraskevopoulos, Alexandros Potamianos

In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks. We introduce UDALM, a fine-tuning procedure, using a mixed classifi…