scholarly journals SEQUENCE-TO-SEQUENCE LEARNING FOR MOTION-AWARE CLAIM GENERATION

2020 ◽  
pp. 620-628
Author(s):  
Derwin Suhartono ◽  
Aryo Pradipta Gema ◽  
Suhendro Winton ◽  
Theodorus David ◽  
Mohamad Ivan Fanany ◽  
...  
Keyword(s):  
Sequence Learning ◽  
Deep Neural Network ◽  
Sampling Strategy ◽  
Claim Data ◽  
Learning Method ◽  
Grammatical Structure ◽  
Neural Network Approach ◽  
Novel Approach ◽  
Argumentation Mining ◽  
Proposed Model

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.

scholarly journals A Neural Network Approach for Birds of a Feather Solvability Prediction

2019 ◽  
Vol 33 ◽  
pp. 9706-9712
Author(s):  
Benjamin Sang ◽  
Sejong Yoon
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Machine Learning Method ◽  
Card Game ◽  
Learning Method ◽  
Solution Path ◽  
Network Approach ◽  
Neural Network Approach ◽  
Search Spaces

Birds of a Feather is a single player, perfect information card game. The game can have multiple board sizes with larger boards introducing larger search spaces that grow exponentially. In this paper, we investigate the solvability of the game, aiming at building a machine learning method to automatically classify whether a given board state has a solution path or not. We propose a method based on image-based features of the board state and deep neural network. Experimental results show that the proposed method can make reasonable predictions of the solvability of a game at an arbitrary stage of the game.

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

scholarly journals An optimized Deep Neural Network Approach for Vehicular Traffic Noise Trend Modelling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed Abdulkareem Ahmed ◽  
Biswajeet Pradhan ◽  
Subrata Chakraborty ◽  
Abdullah Alamri ◽  
Chang-Wook Lee
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Traffic Noise ◽  
Vehicular Traffic ◽  
Network Approach ◽  
Neural Network Approach

Food waste treatment option selection through spherical fuzzy AHP

2021 ◽  
pp. 1-11
Author(s):  
Aysu Melis Buyuk ◽  
Gul T. Temur
Keyword(s):  
Treatment Option ◽  
Food Waste ◽  
Waste Treatment ◽  
Fuzzy Ahp ◽  
Treatment Options ◽  
Analytic Hierarchy ◽  
Food Treatment ◽  
Novel Approach ◽  
Proposed Model ◽  
Option Selection

In line with the increase in consciousness on sustainability in today’s global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions.

Validating the impact of accounting disclosures on stock market: A deep neural network approach

2021 ◽  
Vol 170 ◽  
pp. 120903
Author(s):  
Prajwal Eachempati ◽  
Praveen Ranjan Srivastava ◽  
Ajay Kumar ◽  
Kim Hua Tan ◽  
Shivam Gupta
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Deep Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Accounting Disclosures ◽  
The Impact
Sign in / Sign up

Export Citation Format

Share Document