In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRTbased proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student’s future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on. A hierarchical extension of IRT that captured item grouping structure performed best overall. When data sets included non-trivial autocorrelations in student response patterns, a temporal extension of IRT improved performance over standard IRT while the RNNbased method did not. We conclude that IRT-based models provide a simpler, better-performing alternative to existing RNN-based models of student interaction data while also affording more interpretability and guarantees due to their formulation as Bayesian probabilistic models.

Output: Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation


Input: Consider Input: This paper is an empirical study of the distributed deep learning for a question answering subtask: answer selection. Comparison studies of SGD, MSGD, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results show that the message passing interface based distributed framework can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training: with 120 workers, an 83x speedup is achievable and running time is decreased from 107.9 hours to 1.3 hours, which will benefit the productivity significantly.

Output: Distributed Deep Learning for Answer Selection


Input: Consider Input: We consider the problem of search through comparisons, where a user is presented with two candidate objects and reveals which is closer to her intended target. We study adaptive strategies for finding the target, that require knowledge of rank relationships but not actual distances between objects. We propose a new strategy based on rank nets, and show that for target distributions with a bounded doubling constant, it finds the target in a number of comparisons close to the entropy of the target distribution and, hence, of the optimum. We extend these results to the case of noisy oracles, and compare this strategy to prior art over multiple datasets.


Output: Comparison-Based Learning with Rank Nets