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.

Asking questions is an important ability for a chatbot. This paper focuses on question generation. Although there are existing works on question generation based on a piece of descriptive text, it remains to be a very challenging problem. In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text. The key reason for proposing the new problem is that in practical applications, we found that useful questions need to be targeted toward some relevant topics. One almost never asks a random question in a conversation. Due to the fact that given a descriptive text, it is often possible to ask many types of questions, generating a question without knowing what it is about is of limited use. To solve the problem, we propose a novel neural network that is able to generate topic-specific questions. One major advantage of this model is that it can be trained directly using a question-answering corpus without requiring any additional annotations like annotating topics in the questions or answers. Experimental results show that our model outperforms the state-of-the-art baseline.
We propose a neural network that is able to generate topic-specific questions.

Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques.
A unified statistical view of the broad class of deep generative models 

Up until very recently, inspired by a mass of researches on adversarial examples for computer vision, there has been a growing interest in designing adversarial attacks for Natural Language Processing (NLP) tasks, followed by very few works of adversarial defenses for NLP. To our knowledge, there exists no defense method against the successful synonym substitution based attacks that aim to satisfy all the lexical, grammatical, semantic constraints and thus are hard to perceived by humans. We contribute to fill this gap and propose a novel adversarial defense method called Synonym Encoding Method (SEM), which inserts an encoder before the input layer of the model and then trains the model to eliminate adversarial perturbations. Extensive experiments demonstrate that SEM can efficiently defend current best synonym substitution based adversarial attacks with little decay on the accuracy for benign examples. To better evaluate SEM, we also design a strong attack method called Improved Genetic Algorithm (IGA) that adopts the genetic metaheuristic for synonym substitution based attacks. Compared with existing genetic based adversarial attack, IGA can achieve higher attack success rate while maintaining the transferability of the adversarial examples.

The first text adversarial defense method in word level, and the improved generic based attack method against synonyms substitution based attacks.

