instruction:
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.
question:
Learning effective text representations is a key foundation for numerous machine learning and NLP applications. While the celebrated Word2Vec technique yields semantically rich word representations, it is less clear whether sentence or document representations should be built upon word representations or from scratch. Recent work has demonstrated that a distance measure between documents called \emph{Word Mover's Distance} (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy. However, WMD is very expensive to compute, and is harder to apply beyond simple KNN than feature embeddings. In this paper, we propose the \emph{Word Mover's Embedding } (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings. Our technique extends the theory of \emph{Random Features} to show convergence of the inner product between WMEs to a positive-definite kernel that can be interpreted as a soft version of (inverse) WMD. The proposed embedding is more efficient and flexible than WMD in many situations. As an example, WME with a simple linear classifier reduces the computational cost of WMD-based KNN \emph{from cubic to linear} in document length and \emph{from quadratic to linear} in number of samples, while simultaneously improving accuracy. In experiments on 9 benchmark text classification datasets and 22 textual similarity tasks the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.
answer:
A novel approach to building an unsupervised document (sentence) embeddings from pre-trainedword embeddings


question:
We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay. In our experiments on neural networks for image classification, speech recognition, machine translation, and language modeling, it performs on par or better than well tuned SGD with momentum and Adam/AdamW. 
 Additionally, NovoGrad (1) is robust to the choice of learning rate and weight initialization, (2) works well in a large batch setting, and (3) has two times smaller memory footprint than Adam.
answer:
NovoGrad -  an adaptive SGD method with layer-wise gradient normalization and decoupled weight decay. 


question:
Sound correspondence patterns play a crucial role for linguistic reconstruction. Linguists
 use them to prove language relationship, to reconstruct proto-forms, and for classical
 phylogenetic reconstruction based on shared innovations.Cognate words which fail to
 conform with expected patterns can further point to various kinds of exceptions in sound
 change, such as analogy or assimilation of frequent words. Here we present an automatic
 method for the inference of sound correspondence patterns across multiple languages based
 on a network approach. The core idea is to represent all columns in aligned cognate sets as
 nodes in a network with edges representing the degree of compatibility between the nodes.
 The task of inferring all compatible correspondence sets can then be handled as the well-known minimum clique cover problem in graph theory, which essentially seeks to split the graph into the smallest number of cliques in which each node is represented by exactly one clique. The resulting partitions represent all correspondence patterns which can be
 inferred for a given dataset. By excluding those patterns which occur in only a few cognate
 sets, the core of regularly recurring sound correspondences can be inferred. Based on this
 idea, the paper presents a method for automatic correspondence pattern recognition, which
 is implemented as part of a Python library which supplements the paper. To illustrate the
 usefulness of the method, various tests are presented, and concrete examples of the output
 of the method are provided. In addition to the source code, the study is supplemented by
 a short interactive tutorial that illustrates how to use the new method and how to inspect
 its results.
answer:

The paper describes a new algorithm by which sound correspondence patterns for multiple languages can be inferred.