Argument annotation and analysis using deep learning with attention mechanism in Bahasa Indonesia

Abstract

Argumentation mining is a research field which focuses on sentences in type of argumentation. Argumentative sentences are often used in daily communication and have important role in each decision or conclusion making process. The research objective is to do observation in deep learning utilization combined with attention mechanism for argument annotation and analysis. Argument annotation is argument component classification from certain discourse to several classes. Classes include major claim, claim, premise and non-argumentative. Argument analysis points to argumentation characteristics and validity which are arranged into one topic. One of the analysis is about how to assess whether an established argument is categorized as sufficient or not. Dataset used for argument annotation and analysis is 402 persuasive essays. This data is translated into Bahasa Indonesia (mother tongue of Indonesia) to give overview about how it works with specific language other than English. Several deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit) are utilized for argument annotation and analysis while HAN (Hierarchical Attention Network) is utilized only for argument analysis. Attention mechanism is combined with the model as weighted access setter for a better performance. From the whole experiments, combination of deep learning and attention mechanism for argument annotation and analysis arrives in a better result compared with previous research.

Argael: Argument Annotation and Evaluation Tool

“…Several argument models have been proposed in the literature, among which the traditional premise-claim model with support-attack relations stands out [4,31,32].…”

Multi-Task Attentive Residual Networks for Argument Mining

“…Among the AM systems that use neural attention, the one used in [29] integrate hierarchical attention and biGRU for the analysis of the quality of the argument, the one in [30] use attention to integrate sentiment lexicon, while in other works [31], [32], [33] attention modules are stacked on top of recurrent layers. The use of Pointer Networks for AM has also been investigated [34].…”

Multi-Task Attentive Residual Networks for Argument Mining

“…Among the AM systems that use neural attention, the one used in [18] integrate hierarchical attention and biGRU for the analysis of the quality of the argument, the one in [19] use attention to integrate sentiment lexicon, while in other works [20]- [22] attention modules are stacked on top of recurrent layers. The use of Pointer Networks for AM has also been investigated [23].…”