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20182023
most citedSEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression

22 citations · 73 across the 10 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

cs.SD2018★ 21 cited

Integrating Recurrence Dynamics for Speech Emotion Recognition

Efthymios Tzinis, Georgios Paraskevopoulos, Christos Baziotis +1

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). Reconstruc…

cs.CL2018

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…

cs.CL2018★ 1 cited

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…

cs.CL2018★ 3 cited

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…

cs.CL2018

NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention

Christos Baziotis, Nikos Athanasiou, Georgios Paraskevopoulos +3

In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction". We participated in subtask A, in which we are called to predict…