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: Distributional Semantics Models(DSM) derive word space from linguistic items
in context. Meaning is obtained by defining a distance measure between vectors
corresponding to lexical entities. Such vectors present several problems. This
work concentrates on quality of word embeddings, improvement of word embedding
vectors, applicability of a novel similarity metric used 'on top' of the
word embeddings. In this paper we provide comparison between two methods
for post process improvements to the baseline DSM vectors. The counter-fitting
method which enforces antonymy and synonymy constraints into the Paragram
vector space representations recently showed improvement in the vectors' capability
for judging semantic similarity. The second method is our novel RESM
method applied to GloVe baseline vectors. By applying the hubness reduction
method, implementing relational knowledge into the model by retrofitting synonyms
and providing a new ranking similarity definition RESM that gives maximum
weight to the top vector component values we equal the results for the ESL
and TOEFL sets in comparison with our calculations using the Paragram and Paragram
+ Counter-fitting methods. For SIMLEX-999 gold standard since we cannot
use the RESM the results using GloVe and PPDB are significantly worse compared
to Paragram. Apparently, counter-fitting corrects hubness. The Paragram
or our cosine retrofitting method are state-of-the-art results for the SIMLEX-999
gold standard. They are 0.2 better for SIMLEX-999 than word2vec with sense
de-conflation (that was announced to be state-of the-art method for less reliable
gold standards). Apparently relational knowledge and counter-fitting is more important
for judging semantic similarity than sense determination for words. It is to
be mentioned, though that Paragram hyperparameters are fitted to SIMLEX-999
results. The lesson is that many corrections to word embeddings are necessary
and methods with more parameters and hyperparameters perform better.

A:
Paper provides a description of a procedure to enhance word vector space model with an evaluation of Paragram and GloVe models for Similarity Benchmarks.