scholarly journals Distributed Representations for Arithmetic Word Problems

2020 ◽  
Vol 34 (05) ◽  
pp. 9000-9007
Author(s):  
Sowmya S Sundaram ◽  
Deepak P ◽  
Savitha Sam Abraham
Keyword(s):  
Learning Process ◽  
Word Problems ◽  
Representation Learning ◽  
Learning Method ◽  
Distributed Representations ◽  
Convolutional Networks ◽  
Neural Architecture ◽  
Wide Range ◽  
Learning Platforms ◽  
Embedding Methods

We consider the task of learning distributed representations for arithmetic word problems. We outline the characteristics of the domain of arithmetic word problems that make generic text embedding methods inadequate, necessitating a specialized representation learning method to facilitate the task of retrieval across a wide range of use cases within online learning platforms. Our contribution is two-fold; first, we propose several 'operators' that distil knowledge of the domain of arithmetic word problems and schemas into word problem transformations. Second, we propose a novel neural architecture that combines LSTMs with graph convolutional networks to leverage word problems and their operator-transformed versions to learn distributed representations for word problems. While our target is to ensure that the distributed representations are schema-aligned, we do not make use of schema labels in the learning process, thus yielding an unsupervised representation learning method. Through an evaluation on retrieval over a publicly available corpus of word problems, we illustrate that our framework is able to consistently improve upon contemporary generic text embeddings in terms of schema-alignment.

scholarly journals Text-Graph Enhanced Knowledge Graph Representation Learning

2021 ◽  
Vol 4 ◽  
Author(s):  
Linmei Hu ◽  
Mengmei Zhang ◽  
Shaohua Li ◽  
Jinghan Shi ◽  
Chuan Shi ◽  
...  
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Graph Representation ◽  
Semantic Relationships ◽  
Convolutional Networks ◽  
Gating Mechanism ◽  
Benchmark Datasets ◽  
Knowledge Graphs ◽  
Low Dimensional ◽  
Embedding Methods

Knowledge Graphs (KGs) such as Freebase and YAGO have been widely adopted in a variety of NLP tasks. Representation learning of Knowledge Graphs (KGs) aims to map entities and relationships into a continuous low-dimensional vector space. Conventional KG embedding methods (such as TransE and ConvE) utilize only KG triplets and thus suffer from structure sparsity. Some recent works address this issue by incorporating auxiliary texts of entities, typically entity descriptions. However, these methods usually focus only on local consecutive word sequences, but seldom explicitly use global word co-occurrence information in a corpus. In this paper, we propose to model the whole auxiliary text corpus with a graph and present an end-to-end text-graph enhanced KG embedding model, named Teger. Specifically, we model the auxiliary texts with a heterogeneous entity-word graph (called text-graph), which entails both local and global semantic relationships among entities and words. We then apply graph convolutional networks to learn informative entity embeddings that aggregate high-order neighborhood information. These embeddings are further integrated with the KG triplet embeddings via a gating mechanism, thus enriching the KG representations and alleviating the inherent structure sparsity. Experiments on benchmark datasets show that our method significantly outperforms several state-of-the-art methods.

APPLICATION OF HYPNOTEACHING METHOD IN SPIRITUAL VALUES LEARNING

2019 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Athoillah Islamy ◽  
Nurul Istiani
Keyword(s):  
Qualitative Research ◽  
Learning Process ◽  
Teaching And Learning ◽  
Primary Source ◽  
Learning Method ◽  
Spiritual Values ◽  
Library Research ◽  
Spiritual Learning

This study aims to explain the application of hypnoteaching method in spiritual values learning. This type of research is library research. This research is qualitative research. The primary source used in this study is the thought of Muhammad Noer in his book entitled Hypnoteaching For Success Learning. This research concludes that the hypnoteaching method is a learning method that combines teaching and learning with hypnosis. This method can be used as one of the methods in the process of learning spiritual values. In its application, the hypnoteaching method emphasizes the cognitive, affective and psychomotor aspects of students through positive suggestions. With these steps, it is expected to create a more effective and enjoyable spiritual learning process. Keywords: Method, Hypnoteaching, Learning, Spiritual

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Shengsheng Qian ◽  
Jun Hu ◽  
Quan Fang ◽  
Changsheng Xu
Keyword(s):  
Social Media ◽  
Visual Information ◽  
Representation Learning ◽  
Fake News ◽  
Unified Framework ◽  
Model Learning ◽  
Convolutional Network ◽  
Textual Information ◽  
Convolutional Networks ◽  
Real World Datasets

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

Exploiting Graph Convolutional Networks for Representation Learning of Mobile App Usage

2019 ◽  
Author(s):  
Keiichi Ochiai ◽  
Naoki Yamamoto ◽  
Takashi Hamatani ◽  
Yusuke Fukazawa ◽  
Takayasu Yamaguchi
Keyword(s):  
Representation Learning ◽  
Mobile App ◽  
Convolutional Networks

scholarly journals A strategy learning model for autonomous agents based on classification

2015 ◽  
Vol 25 (3) ◽  
pp. 471-482 ◽  
Author(s):  
Bartłomiej Śnieżyński
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Autonomous Agents ◽  
Good Alternative ◽  
Learning Model ◽  
Learning Method ◽  
Complex Environments ◽  
Agent Based ◽  
Proposed Model

AbstractIn this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

scholarly journals PENDAMPINGAN TERHADAP GURU DALAM MENERAPKAN METODE PEMBELAJARAN YANG EFEKTIF DAN INOVATIF DALAM MENARIK MINAT BELAJAR SISWA DI MI NASRUL ULUM KLAGENSRAMPAT PADA MASA PANDEMI COVID-19

2021 ◽  
Vol 3 (2) ◽  
pp. 193-210
Author(s):  
Ida Latifatul Umroh ◽  
Khotimah Suryani ◽  
Ita Hidayatus Sholihah ◽  
Krisna Andika ◽  
Rihlatulillah
Keyword(s):  
Learning Process ◽  
Teaching And Learning ◽  
Learning Activity ◽  
Learning Method ◽  
Match Method ◽  
Learning Methods ◽  
Innovative Teaching ◽  
Learning Interest

The purpose  of this activity is for increasing  students interest  in learning  at MI Nasrul  Ulum Klagensrampat. This activity uses a learning  method in conveying material  to students.By applying this method the writer hopes that the learning process can be achieved properly. Therefore, it is very important for educators  to recognize several kinds of learning  methods so that students feel happy and comfortable when learning takes place. The writing of this work were supported by the researcher’s activity after doing identification  and communication with the teacher,  so that the researchers and the teacher  were able to find out the obstacles faced by the students in teaching and learning  activity which has been done. In this activity, the researcher applied various methods of effective and innovative teaching which were able to increase students learning interest. The learning methods are puzzle method, flashcard method, silent way method, make a match method, snowball method, bamboo method, and monopoly method

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