Amobee at IEST 2018: Transfer Learning from Language Models (1808.08782v2)

Abstract: This paper describes the system developed at Amobee for the WASSA 2018 implicit emotions shared task (IEST). The goal of this task was to predict the emotion expressed by missing words in tweets without an explicit mention of those words. We developed an ensemble system consisting of LLMs together with LSTM-based networks containing a CNN attention mechanism. Our approach represents a novel use of LLMs (specifically trained on a large Twitter dataset) to predict and classify emotions. Our system reached 1st place with a macro $\text{F}_1$ score of 0.7145.