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 present a new unsupervised method for learning general-purpose sentence embeddings.
 Unlike existing methods which rely on local contexts, such as words
 inside the sentence or immediately neighboring sentences, our method selects, for
 each target sentence, influential sentences in the entire document based on a document
 structure. We identify a dependency structure of sentences using metadata
 or text styles. Furthermore, we propose a novel out-of-vocabulary word handling
 technique to model many domain-specific terms, which were mostly discarded by
 existing sentence embedding methods. We validate our model on several tasks
 showing 30% precision improvement in coreference resolution in a technical domain,
 and 7.5% accuracy increase in paraphrase detection compared to baselines.
[A]: To train a sentence embedding using technical documents, our approach considers document structure to find broader context and handle out-of-vocabulary words.


[Q]: Unsupervised bilingual dictionary induction (UBDI) is useful for unsupervised machine translation and for cross-lingual transfer of models into low-resource languages. One approach to UBDI is to align word vector spaces in different languages using Generative adversarial networks (GANs) with linear generators, achieving state-of-the-art performance for several language pairs. For some pairs, however, GAN-based induction is unstable or completely fails to align the vector spaces. We focus on cases where linear transformations provably exist, but the performance of GAN-based UBDI depends heavily on the model initialization. We show that the instability depends on the shape and density of the vector sets, but not on noise; it is the result of local optima, but neither over-parameterization nor changing the batch size or the learning rate consistently reduces instability. Nevertheless, we can stabilize GAN-based UBDI through best-of-N model selection, based on an unsupervised stopping criterion.
[A]: An empirical investigation of GAN-based alignment of word vector spaces, focusing on cases, where linear transformations provably exist, but training is unstable.


[Q]: It is well known that it is possible to construct "adversarial examples"
 for neural networks: inputs which are misclassified by the network
 yet indistinguishable from true data. We propose a simple
 modification to standard neural network architectures, thermometer
 encoding, which significantly increases the robustness of the network to
 adversarial examples. We demonstrate this robustness with experiments
 on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show that
 models with thermometer-encoded inputs consistently have higher accuracy
 on adversarial examples, without decreasing generalization.
 State-of-the-art accuracy under the strongest known white-box attack was 
 increased from 93.20% to 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10.
 We explore the properties of these networks, providing evidence
 that thermometer encodings help neural networks to
 find more-non-linear decision boundaries.
[A]:
Input discretization leads to robustness against adversarial examples