2018
EMNLP
EMNLP 2018
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
Abstract
AbstractIn this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2nd place out of 30 teams with a test macro F1 score of 0.710. The system is composed of a single pre-trained ELMo layer for encoding words, a Bidirectional Long-Short Memory Network BiLSTM for enriching word representations with context, a max-pooling operation for creating sentence representations from them, and a Dense Layer for projecting the sentence representations into label space. Our official submission was obtained by ensembling 6 of these models initialized with different random seeds. The code for replicating this paper is available at https://github.com/jabalazs/implicit_emotion.
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Learning Types > Self-Supervised Learning
Deep Learning > Architectures > Transformers
Natural Language Processing > Applications > Sentiment Analysis
Deep Learning > Learning Types > Representation Learning
Deep Learning > Architectures > Recurrent Neural Networks