Lyb3b at SemEval-2018 Task 12: Ensemble-based Deep Learning Models for Argument Reasoning Comprehension Task

Abstract

Reasoning is a crucial part of natural language argumentation. In order to comprehend an argument, we have to reconstruct and analyze its reasoning. In this task, given a natural language argument with a reason and a claim, the goal is to choose the correct implicit reasoning from two options, in order to form a reasonable structure of (Reason, Warrant, Claim). Our approach is to build distributed word embedding of reason, warrant and claim respectively, meanwhile, we use a series of frameworks such as CNN model, LSTM model, GRU with attention model and biLSTM with attention model for processing word vector. Finally, ensemble mechanism is used to integrate the results of each framework to improve the final accuracy. Experiments demonstrate superior performance of ensemble mechanism compared to each separate framework. We are the 11th in official results, the final model can reach a 0.568 accuracy rate on the test dataset.