Attention-based argumentation mining

Suhartono, Derwin; Gema, Aryo Pradipta; Winton, Suhendro; David, Theodorus; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

doi:10.1504/ijcvr.2019.102282

Cited by 1 publication

(4 citation statements)

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“…Word vectors utilization Based on the experiments conducted, performance of FastText is worse than word embeddings from scratch. It is in line with previous research using English dataset Table 7 Result of argument analysis using hierarchical attention network (Word Embedding from Scratch) [43]. We arrived in a conclusion that pre-trained word vector is not suitable to work on argumentative statements.…”

Section: B Argument Analysissupporting

confidence: 89%

“…Best model in argument analysis is HAN with word embedding from scratch with 64 as batch size. This result is in line with experiment using English dataset [43]. HAN has a good performance in dataset with hierarchical characteristics.…”

Section: B Argument Analysissupporting

confidence: 89%

“…Comparing our result with previous similar research utilizing English dataset [43], there is no extreme differences. F1 and ROC-AUC score are relatively close.…”

Section: Form Of Languagesupporting

confidence: 71%

“…Aside from it, we did not use word vector yet utilizing embedding layer (build vector from scratch, without using pre-trained word vector) to compare the performance. Previously, similar works using English dataset was conducted [43] and Glove as word vector representation was used. This research continues to investigate the result from specific language, which is in Bahasa Indonesia.…”

Section: Argument Analysismentioning

confidence: 99%

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

et al. 2020

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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.

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Section: B Argument Analysissupporting

confidence: 89%

Section: B Argument Analysissupporting

confidence: 89%

“…Comparing our result with previous similar research utilizing English dataset [43], there is no extreme differences. F1 and ROC-AUC score are relatively close.…”

Section: Form Of Languagesupporting

confidence: 71%

Section: Argument Analysismentioning

confidence: 99%

See 2 more Smart Citations