Multi-Context Reasoning for Question Answering


Multi-Context Reasoning for Question Answering – This paper deals with the problem of Answer Processing (AP) in a context-aware setting. In this context we refer to the context-aware semantic processing task which involves inferring the relevant information from a sentence. However, there are few clear criteria that can achieve the best scores for an appropriate task without the knowledge or ability of the human reader. To address this, this paper presents a new framework to model task-specific semantic information from a corpus using multi-scale attention mechanism. The framework is based on a novel method that we call Multi-Selection-Context Multiparameter Attention (M-CEAM). Our system generates sentences in a high dimensional context with multi-scale attention mechanism, but the task is different from typical human-authored text. We provide an efficient implementation of our framework by means of a supervised training and annotation pipeline for our system. In our experimental results, we show that M-CEAM outperforms state-of-the-art semantic and inference-based approaches on several tasks.

One of the most important problems in machine learning is to model the data in large enough quantities to generate high-quality predictions. In this work, we describe the method of learning from a large dataset of 2D structured images. The dataset consists of more than 10,000 images of 1,500 subjects. During the training phase, our method learns to predict the subjects’ pose, pose-based poses, and pose-based poses separately (e.g., with a normal pose). The learning process is a recurrent neural network (RNN) with 2d masks and 2d data. After that, our method can classify images. We develop a convolutional network for this task, namely, a network of deep recurrent neurons which simultaneously performs different tasks over pairs in the 2D and 3D. We evaluate the performance of the network by running it in the test dataset of 100 subjects in a video analysis lab. Experiments show that our model outperforms state-of-the-art models and significantly outperforms state-of-the-art models for different tasks.

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Multi-Context Reasoning for Question Answering

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  • Adversarial Data Analysis in Multi-label Classification

    Adaptive Neural Network-based ClassificationOne of the most important problems in machine learning is to model the data in large enough quantities to generate high-quality predictions. In this work, we describe the method of learning from a large dataset of 2D structured images. The dataset consists of more than 10,000 images of 1,500 subjects. During the training phase, our method learns to predict the subjects’ pose, pose-based poses, and pose-based poses separately (e.g., with a normal pose). The learning process is a recurrent neural network (RNN) with 2d masks and 2d data. After that, our method can classify images. We develop a convolutional network for this task, namely, a network of deep recurrent neurons which simultaneously performs different tasks over pairs in the 2D and 3D. We evaluate the performance of the network by running it in the test dataset of 100 subjects in a video analysis lab. Experiments show that our model outperforms state-of-the-art models and significantly outperforms state-of-the-art models for different tasks.


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