22 citations · 108 across the 43 of their papers we have counts for
26 papers · 1 filter
The Illusion of Balanced Multimodal Sentiment Analysis: Beyond the Limits of Optimization-Based Methods
Ioanna Kaffeza, Efthymios Georgiou, Alexandros Potamianos
Multimodal Sentiment Analysis (MSA) remains constrained by modality imbalance, yet the field continues to rely on optimization-based balancing methods that promise more than they d…
Segregate, Refine, Integrate: Decomposing Multimodal Fusion for Sentiment Analysis
Alexios Filippakopoulos, Elias Kallioras, Nikolaos Xiros +2
Multimodal fusion must simultaneously refine modality-specific signals and model cross-modal interactions; two competing objectives typically entangled within the same operation. W…
Masked Diffusion Language Models with Frequency-Informed Training
Despoina Kosmopoulou, Efthymios Georgiou, Vaggelis Dorovatas +2
We present a masked diffusion language modeling framework for data-efficient training for the BabyLM 2025 Challenge. Our approach applies diffusion training objectives to language…
MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR
Dimitrios Damianos, Georgios Paraskevopoulos, Alexandros Potamianos
In this work, we investigate the Meta PL unsupervised domain adaptation framework for Automatic Speech Recognition (ASR). We introduce a Multi-Stage Domain Adaptation pipeline (MSD…
MEDUSA: A Multimodal Deep Fusion Multi-Stage Training Framework for Speech Emotion Recognition in Naturalistic Conditions
Georgios Chatzichristodoulou, Despoina Kosmopoulou, Antonios Kritikos +5
SER is a challenging task due to the subjective nature of human emotions and their uneven representation under naturalistic conditions. We propose MEDUSA, a multimodal framework wi…
DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis
Efthymios Georgiou, Vassilis Katsouros, Yannis Avrithis +1
While multimodal fusion has been extensively studied in Multimodal Sentiment Analysis (MSA), the role of fusion depth and multimodal capacity allocation remains underexplored. In t…
Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors
Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman +1
In-context Learning (ICL) has become the primary method for performing natural language tasks with Large Language Models (LLMs). The knowledge acquired during pre-training is cruci…
BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom's-Taxonomy-Inspired Prompts
Maria-Eleni Zoumpoulidi, Georgios Paraskevopoulos, Alexandros Potamianos
Despite the remarkable capabilities of large language models (LLMs) across a range of tasks, mathematical reasoning remains a challenging frontier. Motivated by the observation tha…
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman +1
In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) without updating the models' parameters, in cont…
PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis
Efthymios Georgiou, Yannis Avrithis, Alexandros Potamianos
Multimodal sentiment analysis (MSA) leverages heterogeneous data sources to interpret the complex nature of human sentiments. Despite significant progress in multimodal architectur…
SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method
Efthymios Georgiou, Alexandros Potamianos
Data augmentation is a prevalent technique for improving performance in various machine learning applications. We propose SeqAug, a modality-agnostic augmentation method that is ta…
Depression detection in social media posts using affective and social norm features
Ilias Triantafyllopoulos, Georgios Paraskevopoulos, Alexandros Potamianos
We propose a deep architecture for depression detection from social media posts. The proposed architecture builds upon BERT to extract language representations from social media po…
Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek
Georgios Paraskevopoulos, Theodoros Kouzelis, Georgios Rouvalis +3
Modern speech recognition systems exhibits rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource langua…
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…
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
Eleftherios Kapelonis, Efthymios Georgiou, Alexandros Potamianos
Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations. Recent state-of-the-art DST implementations rely on schemata of d…
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…
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…
Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews
D. Xezonaki, G. Paraskevopoulos, A. Potamianos +1
In this work we propose a machine learning model for depression detection from transcribed clinical interviews. Depression is a mental disorder that impacts not only the subject's…
Cross-topic distributional semantic representations via unsupervised mappings
Eleftheria Briakou, Nikos Athanasiou, Alexandros Potamianos
In traditional Distributional Semantic Models (DSMs) the multiple senses of a polysemous word are conflated into a single vector space representation. In this work, we propose a DS…
SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
Christos Baziotis, Ion Androutsopoulos, Ioannis Konstas +1
Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-se…
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models
Alexandra Chronopoulou, Christos Baziotis, Alexandros Potamianos
A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora. In this paper we present a conceptually simple and effect…
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
Alexandra Chronopoulou, Aikaterini Margatina, Christos Baziotis +1
In this paper we present our approach to tackle the Implicit Emotion Shared Task (IEST) organized as part of WASSA 2018 at EMNLP 2018. Given a tweet, from which a certain word has…
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
Christos Baziotis, Nikos Athanasiou, Pinelopi Papalampidi +4
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets". We design and ensemble two independent models, based on…
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
Christos Baziotis, Nikos Athanasiou, Alexandra Chronopoulou +5
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all subtasks for English tweets. We prop…