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Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in the literature. In this paper, we present two training algorithms for learning better QA and QG models through leveraging one another. The first algorithm extends Generative Adversarial Network (GAN), which selectively incorporates artificially generated instances as additional QA training data. The second algorithm is an extension of dual learning, which incorporates the probabilistic correlation of QA and QG as additional regularization in training objectives. To test the scalability of our algorithms, we conduct experiments on both document based and table based question answering tasks. Results show that both algorithms improve a QA model in terms of accuracy and QG model in terms of BLEU score. Moreover, we find that the performance of a QG model could be easily improved by a QA model via policy gradient, however, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model. Our algorithm that selectively assigns labels to generated questions would bring a performance boost. [ABSTRACT FROM AUTHOR]

Rare class problems are common in real-world applications across a wide range of domains. Standard classification algorithms are known to perform poorly in these cases, since they focus on overall classification accuracy. In addition, we have seen a significant increase of data in recent years, resulting in many large scale rare class problems. In this paper, we focus on nonlinear kernel based classification methods expressed as a regularized loss minimization problem. We address the challenges associated with both rare class problems and large scale learning, by 1) optimizing area under curve of the receiver of operator characteristic in the training process, instead of classification accuracy and 2) using a rare class kernel representation to achieve an efficient time and space algorithm. We call the algorithm RankRC. We provide justifications for the rare class representation and experimentally illustrate the effectiveness of RankRC in test performance, computational complexity, and model robustness. [ABSTRACT FROM PUBLISHER]