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

Abstract

This paper describes the CLaC Lab 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 pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification. This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12%.

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“…Text Embedding Layer: Owing to the success of transfer learning and pre-training of language models in NLP, we use Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al, 2019) to encode the texts. BERT has shown to capture more contextual text representations as opposed to methods like word2vec (Hu et al, 2017), GloVe (Xu and Cohen, 2018), ELMO (Mohammadi et al, 2019). We encode each text t to a higher dimensional representation m = BERT(t) ∈ R d where d = 768, obtained by averaging the token level outputs from the final layer of BERT.…”