SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

Abstract

In this paper, we present the SemEval-2019 Task 3-EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading "Why don't you ever text me!" we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes-Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class.

ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

“…The competition consists in classifying the emotion of an utterance given its conversational context. More formally, given a textual user utterance along with 2 turns of context in a conversation, the task is to classify the emotion of user utterance as Happy, Sad, Angry or Others (Chatterjee et al, 2019b). The conversations are extracted from Twitter.…”

NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning

“…Results are presented in Table 1 and compared with different approaches. EmoContext organisers proposed a baseline classifier (referred as Baseline) that exhibits a f1-score of 0.587 (Chatterjee et al, 2019). In Table 1, we compare the proposed method with the S-LSTM and F-DNN implementations, described in 3.4 and 3.5, respectively.…”

EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in Text

“…The composition of the dataset is described in Table 1. More details about the task can be found in the task description paper (Chatterjee et al, 2019).…”