Search Results for author:
Found 31 papers, 17 papers with code
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
The promise of ICL is that the LLM can adapt to perform the present task at a competitive or state-of-the-art level at a fraction of the cost.
PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) leverages heterogeneous data sources to interpret the complex nature of human sentiments.
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively.
From West to East: Who can understand the music of the others better?
This leads to research questions on whether these models can be used to learn representations for different music cultures and styles, or whether we can build similar music audio embedding models trained on data from different cultures or styles.
Self-Attention Based Generative Adversarial Networks For Unsupervised Video Summarization
Experimental results indicate that using a self-attention mechanism as the frame selection mechanism outperforms the state-of-the-art on SumMe and leads to comparable to state-of-the-art performance on TVSum and COGNIMUSE.
Generative Adversarial Network
Unsupervised Video Summarization
SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method
Data augmentation is a prevalent technique for improving performance in various machine learning applications.
Efficient Audio Captioning Transformer with Patchout and Text Guidance
The results show that the proposed techniques improve the performance of our system and while reducing the computational complexity.
Depression detection in social media posts using affective and social norm features
Motivated by psychological literature we propose to incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme.
Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek
Modern speech recognition systems exhibits rapid performance degradation under domain shift.
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks
Designing powerful adversarial attacks is of paramount importance for the evaluation of $\ell_p$-bounded adversarial defenses.
Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis
Multimodal learning pipelines have benefited from the success of pretrained language models.
A Dataset for Greek Traditional and Folk Music: Lyra
The content has been collected from a Greek documentary series that is available online, where academics present music traditions of Greece with live music and dance performance during the show, along with discussions about social, cultural and musicological aspects of the presented music.
Extending Compositional Attention Networks for Social Reasoning in Videos
We propose a novel deep architecture for the task of reasoning about social interactions in videos.
A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations.
Regotron: Regularizing the Tacotron2 architecture via monotonic alignment loss
Recent deep learning Text-to-Speech (TTS) systems have achieved impressive performance by generating speech close to human parity.
MMLatch: Bottom-up Top-down Fusion for Multimodal Sentiment Analysis
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 (early fusion).
Ranked #7 on
Multimodal Sentiment Analysis
on CMU-MOSEI
(using extra training data)
EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments
Specifically, during finetuning we propose to use three objectives: response language modeling, sentiment understanding, and empathy forcing.
Unsupervised low-rank representations for speech emotion recognition
We examine the use of linear and non-linear dimensionality reduction algorithms for extracting low-rank feature representations for speech emotion recognition.
UDALM: Unsupervised Domain Adaptation through Language Modeling
In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks.
Ranked #1 on
Sentiment Analysis
on Multi-Domain Sentiment Dataset
End-to-end Generative Zero-shot Learning via Few-shot Learning
In this work, we introduce Z2FSL, an end-to-end generative ZSL framework that uses such an approach as a backbone and feeds its synthesized output to a Few-Shot Learning (FSL) algorithm.
Attention-based Conditioning Methods for External Knowledge Integration
This form of conditioning on the attention distribution, enforces the contribution of the most salient words for the task at hand.
Cross-topic distributional semantic representations via unsupervised mappings
In traditional Distributional Semantic Models (DSMs) the multiple senses of a polysemous word are conflated into a single vector space representation.
SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models
A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora.
Integrating Recurrence Dynamics for Speech Emotion Recognition
We investigate the performance of features that can capture nonlinear recurrence dynamics embedded in the speech signal for the task of Speech Emotion Recognition (SER).
Ranked #46 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Recognition in Conversation
Speech Emotion Recognition
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
In this paper we present our approach to tackle the Implicit Emotion Shared Task (IEST) organized as part of WASSA 2018 at EMNLP 2018.
Pattern Search Multidimensional Scaling
We present a novel view of nonlinear manifold learning using derivative-free optimization techniques.
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction".
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets".
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets".
Structural Attention Neural Networks for improved sentiment analysis
We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification.
