Hierarchical Attention Network with XGBoost for Recognizing Insufficiently Supported Argument

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

doi:10.1007/978-3-319-69456-6_15

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…The XGBoost algorithm continuously adds new CART trees, making it an additive model composed of k base learning models [21]. During training, XGBoost predicts the value for sample i after the t-th iteration based on the previous predictions and the t-th tree's model using Equation (19):…”

Section: Xgboostmentioning

confidence: 99%

Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning

Guo,

Cao,

Lin

2024

Agronomy

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Water scarcity is a global problem. Deficit irrigation (DI) reduces evapotranspiration, improving water efficiency in agriculture. Reference evapotranspiration (ET0) is an important factor in determining DI. ET0 forecasting predicts field water consumption and enables proactive irrigation decisions, offering guidance for water resource management. However, implementation of ET0 forecasting faces challenges due to complex calculations and extensive meteorological data requirements. This project aims to develop a machine learning system for ET0 forecasting. The project involves studying ET0 methods and identifying required meteorological parameters. Historical meteorological data and weather forecasts were obtained from meteorological websites and analyzed for accuracy after preprocessing. A machine learning-based model was created to forecast reference crop evapotranspiration. The model’s input parameters were selected through path analysis before it was optimized using Bayesian optimization to reduce overfitting and improve accuracy. Three forecasting models were developed: one based on historical meteorological data, one based on weather forecasts, and one that corrects the weather forecasts. All three models achieved good accuracy, with root mean square errors ranging from 0.52 to 0.81 mm/day. Among them, the model based on weather forecast had the highest accuracy; the RMSE six days before the forecast period was between 0.52 and 0.75 mm/day, and the RMSE on the seventh day of the forecast period was 1.12 mm/day. In summary, this project has established a mathematical model of ET0 prediction based on machine learning, which can achieve more accurate predictions for within a few days.

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Section: Xgboostmentioning

confidence: 99%

Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning

Guo,

Cao,

Lin

2024

Agronomy

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“…Long Short Term Memory (LSTM) as one of promising deep learning method for text was modified involving Siamese network to recognize argumentation relation in persuasive essay [40]. Furthermore, Hierarchical Attention Network (HAN) with XGBoost was utilized to similar task and indicated to be a promising method for hierarchical data [41]. Table 2 summarized all current works in argument analysis which are done so far.…”

Section: Argument Analysismentioning

confidence: 99%

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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“…In argumentation mining, there is a plethora of research that focuses on understanding elements of debating, for instance, research on claim detection, evidence detection, and stance classification. However, research in argumentation mining does not only revolve around classification or detection, but some also focus on qualitative assessment problem, for example, predicting convincingness of an argument using Bidirectional LSTM [19] and assessing the sufficiency of an argument using CNN [20] and HANs and XGBoosts [6].…”

Section: Related Workmentioning

confidence: 99%

“…Fortunately, computational linguistic researchers already exhibited their interests in this research domain [2], for instance, research on claim detection [3], evidence detection [4], and stance classification [5]. Examples of research that attempt to analyze the qualitative behavior of arguments are recognizing insufficiently supported arguments using Hierarchical Attention Networks (HANs) and XGBoosts [6] and recognizing argumentative relations using Siamese Networks [7]. Despite much proliferation in the argumentation recognition research, the argument generation is still not well developed.…”

Section: Introductionmentioning

confidence: 99%

Sequence-to-Sequence Learning for Motion-Aware Claim Generation

Suhartono¹,

Gema²,

Winton³

et al. 2020

IJC

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The goal of this research is to generate a motion-aware claim using a deep neural network approach: sequence-to-sequence learning method. A motion-aware claim is a sentence that is logically correlated to the motion while preserving its grammatical structure. Our proposed model generates a motion-aware claim in a form of one sentence and takes motion as the input also in a form of one sentence. We use a publicly available argumentation mining dataset that contains annotated motion and claim data. In this research, we propose a novel approach for argument generation by employing a scheduled sampling strategy to make the model converge faster. The BLEU scores and questionnaire are used to quantitatively assess the model. Our best model achieves 0.175 ± 0.088 BLEU-4 score. Based on the questionnaire results, we can also derive a conclusion that it is hard for the respondents to differentiate between the human-made and the model-generated arguments.

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Hierarchical Attention Network with XGBoost for Recognizing Insufficiently Supported Argument

Cited by 4 publications

References 31 publications

Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning

Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning

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

Sequence-to-Sequence Learning for Motion-Aware Claim Generation

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