45 citations · 61 across the 10 of their papers we have counts for
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Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)
A. Sutherland, S. Bensch, T. Hellström +2
Sentiment analysis has transitioned from classifying the sentiment of an entire sentence to providing the contextual information of what targets exist in a sentence, what sentiment…
Leveraging Recursive Processing for Neural-Symbolic Affect-Target Associations
A. Sutherland, S. Magg, S. Wermter
Explaining the outcome of deep learning decisions based on affect is challenging but necessary if we expect social companion robots to interact with users on an emotional level. In…
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
Chandrakant Bothe, Cornelius Weber, Sven Magg +1
The recognition of emotion and dialogue acts enriches conversational analysis and help to build natural dialogue systems. Emotion interpretation makes us understand feelings and di…
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples
Marcus Soll, Tobias Hinz, Sven Magg +1
Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans. These adversarial examples are a challenge for…
KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos
Egor Lakomkin, Sven Magg, Cornelius Weber +1
In this paper, we describe KT-Speech-Crawler: an approach for automatic dataset construction for speech recognition by crawling YouTube videos. We outline several filtering and pos…
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis
Egor Lakomkin, Mohammad Ali Zamani, Cornelius Weber +2
Previous work on emotion recognition demonstrated a synergistic effect of combining several modalities such as auditory, visual, and transcribed text to estimate the affective stat…