GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus
This work addresses emotion detection in sentence-level conversational data for NLP applications, but it is incremental as it applies existing methods (GRU with attention) to a specific competition task.
The paper tackled emotion classification in 3-turn conversational data by introducing a Gated Recurrent Neural Network (GRU) model with attention, bootstrapped with contextual information and trained on a multigenre corpus, achieving an overall F1-score of 56.05% in the SemEval-2019 Task 3 competition.
In this paper we present an emotion classifier model submitted to the SemEval-2019 Task 3: EmoContext. The task objective is to classify emotion (i.e. happy, sad, angry) in a 3-turn conversational data set. We formulate the task as a classification problem and introduce a Gated Recurrent Neural Network (GRU) model with attention layer, which is bootstrapped with contextual information and trained with a multigenre corpus. We utilize different word embeddings to empirically select the most suited one to represent our features. We train the model with a multigenre emotion corpus to leverage using all available training sets to bootstrap the results. We achieved overall %56.05 f1-score and placed 144.