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.

Ex Input:
Learning representations of data is an important issue in machine learning. Though GAN has led to significant improvements in the data representations, it still has several problems such as unstable training, hidden manifold of data, and huge computational overhead. GAN tends to produce the data simply without any information about the manifold of the data, which hinders from controlling desired features to generate. Moreover, most of GAN's have a large size of manifold, resulting in poor scalability. In this paper, we propose a novel GAN to control the latent semantic representation, called LSC-GAN, which allows us to produce desired data to generate and learns a representation of the data efficiently. Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables that follow the distribution. As the larger scale of latent space caused by deploying various distributions in one latent space makes training unstable while maintaining the dimension of latent space, we need to separate the process of defining the distributions explicitly and operation of generation. We prove that a VAE is proper for the former and modify a loss function of VAE to map the data into the pre-defined latent space so as to locate the reconstructed data as close to the input data according to its characteristics. Moreover, we add the KL divergence to the loss function of LSC-GAN to include this process. The decoder of VAE, which generates the data with the corresponding features from the pre-defined latent space, is used as the generator of the LSC-GAN. Several experiments on the CelebA dataset are conducted to verify the usefulness of the proposed method to generate desired data stably and efficiently, achieving a high compression ratio that can hold about 24 pixels of information in each dimension of latent space. Besides, our model learns the reverse of features such as not laughing (rather frowning) only with data of ordinary and smiling facial expression.

Ex Output:
We propose a generative model that not only produces data with desired features from the pre-defined latent space but also fully understands the features of the data to create characteristics that are not in the dataset.

Ex Input:
Vision-Language Navigation (VLN) is the task where an agent is commanded to navigate in photo-realistic unknown environments with natural language instructions. Previous research on VLN is primarily conducted on the Room-to-Room (R2R) dataset with only English instructions. The ultimate goal of VLN, however, is to serve people speaking arbitrary languages. Towards multilingual VLN with numerous languages, we collect a cross-lingual R2R dataset, which extends the original benchmark with corresponding Chinese instructions. But it is time-consuming and expensive to collect large-scale human instructions for every existing language. Based on the newly introduced dataset, we propose a general cross-lingual VLN framework to enable instruction-following navigation for different languages. We first explore the possibility of building a cross-lingual agent when no training data of the target language is available. The cross-lingual agent is equipped with a meta-learner to aggregate cross-lingual representations and a visually grounded cross-lingual alignment module to align textual representations of different languages. Under the zero-shot learning scenario, our model shows competitive results even compared to a model trained with all target language instructions. In addition, we introduce an adversarial domain adaption loss to improve the transferring ability of our model when given a certain amount of target language data. Our methods and dataset demonstrate the potentials of building a cross-lingual agent to serve speakers with different languages.

Ex Output:
We introduce a new task and dataset on cross-lingual vision-language navigation, and propose a general cross-lingual VLN framework for the task.

Ex Input:
Attacks on natural language models are difficult to compare due to their different definitions of what constitutes a successful attack. We present a taxonomy of constraints to categorize these attacks. For each constraint, we present a real-world use case and a way to measure how well generated samples enforce the constraint. We then employ our framework to evaluate two state-of-the art attacks which fool models with synonym substitution. These attacks claim their adversarial perturbations preserve the semantics and syntactical correctness of the inputs, but our analysis shows these constraints are not strongly enforced. For a significant portion of these adversarial examples, a grammar checker detects an increase in errors. Additionally, human studies indicate that many of these adversarial examples diverge in semantic meaning from the input or do not appear to be human-written. Finally, we highlight the need for standardized evaluation of attacks that share constraints. Without shared evaluation metrics, it is up to researchers to set thresholds that determine the trade-off between attack quality and attack success. We recommend well-designed human studies to determine the best threshold to approximate human judgement.

Ex Output:
We present a framework for evaluating adversarial examples in natural language processing and demonstrate that generated adversarial examples are often not semantics-preserving, syntactically correct, or non-suspicious.