Classifying Argument Component using Deep Learning on English Dataset

William Gunawan, Derwin Suhartono


The argument component is a part of argumentation mining where the study focused on the type of argument like claim and premise. nowadays there are already various argumentation components datasets, and each dataset has different argument component classes. Our focus is on the performance of deep learning architectures, especially on using contextual embedding as the initial layer of the architecture. As a validation, we use six datasets that have different argument components. And also, this research provides a comprehensive comparison among all deep learning architectures by combining multiple layers of deep learning such as combining the Bidirectional Encoder Representations from Transformers (BERT) base model or word embedding as an embedding layer with LSTM, GRU, or CNN. Related to the results of the research, will be discussed at the end of the journal. After conducting several experiments, significant results were obtained in this study with the BERT-BiGRU-CRF architecture.

Povzetek:      Namen te študije je določiti učinkovitost več modelov na šestih različnih delih podatkov, ki uporabljajo več transformatorjev kot kombinirano ali nezdruženo vdelano plast, kot tudi druge modele globokega učenja.

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