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.

Example input: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.
Example output: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Example explanation: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.
Q: Transferring representations from large-scale supervised tasks to downstream tasks have shown outstanding results in Machine Learning in both Computer Vision and natural language processing (NLP). One particular example can be sequence-to-sequence models for Machine Translation (Neural Machine Translation - NMT). It is because, once trained in a multilingual setup, NMT systems can translate between multiple languages and are also capable of performing zero-shot translation between unseen source-target pairs at test time. In this paper, we first investigate if we can extend the zero-shot transfer capability of multilingual NMT systems to cross-lingual NLP tasks (tasks other than MT, e.g. sentiment classification and natural language inference). We demonstrate a simple framework by reusing the encoder from a multilingual NMT system, a multilingual Encoder-Classifier, achieves remarkable zero-shot cross-lingual classification performance, almost out-of-the-box on three downstream benchmark tasks - Amazon Reviews, Stanford sentiment treebank (SST) and Stanford natural language inference (SNLI). In order to understand the underlying factors contributing to this finding, we conducted a series of analyses on the effect of the shared vocabulary, the training data type for NMT models, classifier complexity, encoder representation power, and model generalization on zero-shot performance. Our results provide strong evidence that the representations learned from multilingual NMT systems are widely applicable across languages and tasks, and the high, out-of-the-box classification performance is correlated with the generalization capability of such systems.
A:
Zero-shot cross-lingual transfer by using multilingual neural machine translation