In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Q: We propose a generative adversarial training approach for the problem of clarification question generation. Our approach generates clarification questions with the goal of eliciting new information that would make the given context more complete. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.


A: We propose an adversarial training approach to the problem of clarification question generation which uses the answer to the question to model the reward. 
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Q: System identification is the process of building a mathematical model of an unknown system from measurements of its inputs and outputs. It is a key step for model-based control, estimator design, and output prediction. This work presents an algorithm for non-linear offline system identification from partial observations, i.e. situations in which the system's full-state is not directly observable. The algorithm presented, called SISL, iteratively infers the system's full state through non-linear optimization and then updates the model parameters. We test our algorithm on a simulated system of coupled Lorenz attractors, showing our algorithm's ability to identify high-dimensional systems that prove intractable for particle-based approaches. We also use SISL to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, we learn a model that predicts the acceleration of the helicopter better than state-of-the-art approaches.

A: This work presents a scalable algorithm for non-linear offline system identification from partial observations.
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Q: Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.
 On the other hand, for many problems, classic algorithms exist, which typically exceed the accuracy and stability of neural networks.
 To combine these two concepts, we present a new kind of neural networks—algorithmic neural networks (AlgoNets).
 These networks integrate smooth versions of classic algorithms into the topology of neural networks.
 Our novel reconstructive adversarial network (RAN) enables solving inverse problems without or with only weak supervision.

A:
Solving inverse problems by using smooth approximations of the forward algorithms to train the inverse models.
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