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CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs

2019-06-01 · SEMEVAL 2019 6 · Ana Valeria Gonz{\'a}lez, Victor Petr{\'e}n Bach Hansen, Joachim Bingel, Anders S{\o}gaard

This work describes the system presented by the CoAStaL Natural Language Processing group at University of Copenhagen. The main system we present uses the same attention mechanism presented in (Yang et al., 2016). Our overall model architecture is also inspired by their hierarchical classification model and adapted to deal with classification in dialogue by encoding information at the turn level. We use different encodings for each turn to create a more expressive representation of dialogue context which is then fed into our classifier.We also define a custom preprocessing step in order to deal with language commonly used in interactions across many social media outlets. Our proposed system achieves a micro F1 score of 0.7340 on the test set and shows significant gains in performance compared to a system using dialogue level encoding.

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General Classification

CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks right

2016-06-01 · SEMEVAL 2016 6 · Joachim Bingel, Natalie Schluter, H{\'e}ctor Mart{\'\i}nez Alonso

Complex Word Identification Lexical Simplification Sentence Compression Text Simplification

SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations

2019-06-01 · SEMEVAL 2019 6 · Flor Miriam Plaza-del-Arco, M. Dolores Molina-Gonz{\'a}lez, Maite Martin, L. Alfonso Ure{\~n}a-L{\'o}pez

Detecting emotions in textual conversation is a challenging problem in absence of nonverbal cues typically associated with emotion, like fa- cial expression or voice modulations. How- ever, more and more users are using …

Emotion Classification General Classification

deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets

2018-06-01 · SEMEVAL 2018 6 · Zi-Yuan Gao, Chia-Ping Chen

This paper describes our system implementation for subtask V-oc of SemEval-2018 Task 1: affect in tweets. We use multi-task learning method to learn shared representation, then learn the features for each task. There are…

Classification General Classification Multi-Task Learning Sentiment Analysis

SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans Detection

2021-08-01 · SEMEVAL 2021 · Micha{\l} Sat{\l}awa, Katarzyna Zam{\l}y{\'n}ska, Jaros{\l}aw Piersa, Joanna Kolis 외

This paper presents a system used for SemEval-2021 Task 5: Toxic Spans Detection. Our system is an ensemble of BERT-based models for binary word classification, trained on a dataset extended by toxic comments modified an…

Classification Toxic Spans Detection

CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM

2019-06-01 · SEMEVAL 2019 6 · Elham Mohammadi, Hessam Amini, Leila Kosseim

This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretraine…

POS Word Embeddings