We explore recently introduced definition modeling technique that provided
the tool for evaluation of different distributed vector representations of
words through modeling dictionary definitions of words. In this work, we study
the problem of word ambiguities in definition modeling and propose a possible
solution by employing latent variable modeling and soft attention mechanisms.
Our quantitative and qualitative evaluation and analysis of the model shows
that taking into account words ambiguity and polysemy leads to performance
improvement.

本文探讨了最近引入的定义建模技术，在模塑字典定义的基础上评估不同分布向量表示的词语，分析了该模型中的词语歧义性问题，并提出了一个潜变量建模和软注意机制的解决方案，我们对该模型进行了定量和定性评估和分析，结果表明考虑到歧义性和多义性会提高性能。

有条件的词汇定义生成器

Conditional Generators of Words Definitions

Distributed vector representations for natural language vocabulary get a lot
of attention in contemporary computational linguistics. This paper summarizes
the experience of applying neural network language models to the task of
calculating semantic similarity for Russian. The experiments were performed in
the course of Russian Semantic Similarity Evaluation track, where our models
took from the 2nd to the 5th position, depending on the task.
We introduce the tools and corpora used, comment on the nature of the shared
task and describe the achieved results. It was found out that Continuous
Skip-gram and Continuous Bag-of-words models, previously successfully applied
to English material, can be used for semantic modeling of Russian as well.
Moreover, we show that texts in Russian National Corpus (RNC) provide an
excellent training material for such models, outperforming other, much larger
corpora. It is especially true for semantic relatedness tasks (although
stacking models trained on larger corpora on top of RNC models improves
performance even more).
High-quality semantic vectors learned in such a way can be used in a variety
of linguistic tasks and promise an exciting field for further study.

该研究论文探讨如何通过神经网络语言模型来计算俄语的语义相似度，并介绍了应用的工具、语料库、实验结果以及学习这种语义向量的潜在用途。

神经语言模型在俄语语义相似性任务中的应用

Texts in, meaning out: neural language models in semantic similarity  task for Russian

The recently introduced continuous Skip-gram model is an efficient method for
learning high-quality distributed vector representations that capture a large
number of precise syntactic and semantic word relationships. In this paper we
present several extensions that improve both the quality of the vectors and the
training speed. By subsampling of the frequent words we obtain significant
speedup and also learn more regular word representations. We also describe a
simple alternative to the hierarchical softmax called negative sampling. An
inherent limitation of word representations is their indifference to word order
and their inability to represent idiomatic phrases. For example, the meanings
of "Canada" and "Air" cannot be easily combined to obtain "Air Canada".
Motivated by this example, we present a simple method for finding phrases in
text, and show that learning good vector representations for millions of
phrases is possible.

本文介绍了连续 Skip-gram 模型的扩展方法，包括减少常见词的子采样、负采样优化、短语学习等，以提高单词向量的质量和训练速度，为解决单词顺序和习惯用语表示上的问题提供了简单的解决方法。