NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs

TL;DR

This paper introduces ensemble deep learning models using word and character level attentive RNNs for irony detection in tweets, achieving top-tier results in SemEval-2018.

Contribution

It presents a novel ensemble of word and character level Bi-LSTM models with self-attention, trained end-to-end without external features, for improved irony detection.

Findings

Achieved 2nd place in initial competition rankings

Enhanced models achieved 1st place with state-of-the-art results

Provided interpretability through attention visualization

Abstract

In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets". We design and ensemble two independent models, based on recurrent neural networks (Bi-LSTM), which operate at the word and character level, in order to capture both the semantic and syntactic information in tweets. Our models are augmented with a self-attention mechanism, in order to identify the most informative words. The embedding layer of our word-level model is initialized with word2vec word embeddings, pretrained on a collection of 550 million English tweets. We did not utilize any handcrafted features, lexicons or external datasets as prior information and our models are trained end-to-end using back propagation on constrained data. Furthermore, we provide visualizations of tweets with annotations for the salient tokens of the attention layer that can help to interpret the inner workings of the proposed models. We ranked 2nd out of 42 teams in Subtask A and 2nd out of 31 teams in Subtask B. However, post-task-completion enhancements of our models achieve state-of-the-art results ranking 1st for both subtasks.