We present a new method for estimating vector space representations of words:
embedding learning by concept induction. We test this method on a highly
parallel corpus and learn semantic representations of words in 1259 different
languages in a single common space. An extensive experimental evaluation on
crosslingual word similarity and sentiment analysis indicates that
concept-based multilingual embedding learning performs better than previous
approaches.

本文提出利用概念归纳进行嵌入式学习的新方法，通过对高度并行的语料库进行测试，学习了 1259 种不同语言的语义表示，并在跨语言单词相似性和情感分析上进行了广泛实验评估，表明基于概念的多语言嵌入学习表现优于以往的方法。

多语言概念归纳的嵌入式学习

Embedding Learning Through Multilingual Concept Induction

In this work, we present a novel counter-fitting method which injects
antonymy and synonymy constraints into vector space representations in order to
improve the vectors' capability for judging semantic similarity. Applying this
method to publicly available pre-trained word vectors leads to a new state of
the art performance on the SimLex-999 dataset. We also show how the method can
be used to tailor the word vector space for the downstream task of dialogue
state tracking, resulting in robust improvements across different dialogue
domains.

该研究提出了一种注入反义词和同义词约束条件以提高向量对语义相似性判断能力的新型 Counter-fitting 方法。将该方法应用于公开预训练的词向量，可在 SimLex-999 数据集上达到新的最高性能水平。研究还展示了如何使用该方法来调整词向量空间以用于下游的对话状态追踪任务，使结果在不同的对话领域都得到了可靠的改进。

将词向量拟合到语言约束条件上的计数器

Counter-fitting Word Vectors to Linguistic Constraints

Recent methods for learning vector space representations of words have
succeeded in capturing fine-grained semantic and syntactic regularities using
vector arithmetic. However, these vector space representations (created through
large-scale text analysis) are typically stored verbatim, since their internal
structure is opaque. Using word-analogy tests to monitor the level of detail
stored in compressed re-representations of the same vector space, the
trade-offs between the reduction in memory usage and expressiveness are
investigated. A simple scheme is outlined that can reduce the memory footprint
of a state-of-the-art embedding by a factor of 10, with only minimal impact on
performance. Then, using the same `bit budget', a binary (approximate)
factorisation of the same space is also explored, with the aim of creating an
equivalent representation with better interpretability.

该论文研究了使用词向量空间中的哈希技术压缩向量表达的内部结构对嵌入式表示存储优化的影响，同时探讨了二元分解对于嵌入式表示的可解释性的提高。

压缩词嵌入

Compressing Word Embeddings

Vector space word representations are learned from distributional information
of words in large corpora. Although such statistics are semantically
informative, they disregard the valuable information that is contained in
semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This
paper proposes a method for refining vector space representations using
relational information from semantic lexicons by encouraging linked words to
have similar vector representations, and it makes no assumptions about how the
input vectors were constructed. Evaluated on a battery of standard lexical
semantic evaluation tasks in several languages, we obtain substantial
improvements starting with a variety of word vector models. Our refinement
method outperforms prior techniques for incorporating semantic lexicons into
the word vector training algorithms.

本文提出了一种基于语义词典的关系信息来改进词向量表示的方法，并在多种语言的标准词汇语义评估任务中获得了显著的改进，其方法超越了以往词向量训练算法中融入语义词典的技术。