Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on
the relative isomorphism of individual embedding spaces. Existing attempts
aimed at controlling the relative isomorphism of different embedding spaces
fail to incorporate the impact of semantically related words in the model
training objective. To address this, we propose GARI that combines the
distributional training objectives with multiple isomorphism losses guided by
the graph attention network. GARI considers the impact of semantical variations
of words in order to define the relative isomorphism of the embedding spaces.
Experimental evaluation using the Arabic language data set shows that GARI
outperforms the existing research by improving the average P@1 by a relative
score of up to 40.95% and 76.80% for in-domain and domain mismatch settings
respectively. We release the codes for GARI at
this https URL

在 NLP 中，双语词汇归纳（BLI）是一个核心挑战，它依赖于个体嵌入空间的相对同构性。我们提出了 GARI，它将分布式训练目标与由图形注意网络指导的多个同构损失相结合，以控制不同嵌入空间的相对同构性，并考虑了单词的语义变化影响以定义嵌入空间的相对同构性，实验结果表明 GARI 在阿拉伯语言数据集上的表现优于现有研究，平均 P@1 在领域内和领域不匹配设置下的相对得分分别提高了 40.95% 和 76.80%。我们在此 https URL 放出了 GARI 的代码。

GARI: 图注意力用于阿拉伯语词向量的相对同构

GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings

We present an experiment in extracting adjectives which express a specific
semantic relation using word embeddings. The results of the experiment are then
thoroughly analysed and categorised into groups of adjectives exhibiting formal
or semantic similarity. The experiment and analysis are performed for English
and Croatian in the domain of karstology using data sets and methods developed
in the TermFrame project. The main original contributions of the article are
twofold: firstly, proposing a new and promising method of extracting
semantically related words relevant for terminology, and secondly, providing a
detailed evaluation of the output so that we gain a better understanding of the
domain-specific semantic structures on the one hand and the types of
similarities extracted by word embeddings on the other.

本文介绍一种使用词嵌入提取具有特定语义关系的形容词的实验，并对其进行了详细分析和分类。作者利用 TermFrame 项目中开发的数据集和方法，在岩溶学领域中使用英语和克罗地亚语进行了实验和分析。本文的两个主要原创贡献是：首先，提出了一种新的、有前景的提取与术语相关的语义词的方法；其次，通过对输出进行详细评估，让我们更好地了解特定领域的语义结构和词嵌入提取的相似性类型。

在喀斯特学领域中，通过词嵌入实现专业形容词的双语翻译

A bilingual approach to specialised adjectives through word embeddings in the karstology domain

Language models have emerged as a central component across NLP, and a great
deal of progress depends on the ability to cheaply adapt them (e.g., through
finetuning) to new domains and tasks. A language model's vocabulary$-$typically
selected before training and permanently fixed later$-$affects its size and is
part of what makes it resistant to such adaptation. Prior work has used
compositional input embeddings based on surface forms to ameliorate this issue.
In this work, we go one step beyond and propose a fully compositional output
embedding layer for language models, which is further grounded in information
from a structured lexicon (WordNet), namely semantically related words and
free-text definitions. To our knowledge, the result is the first word-level
language model with a size that does not depend on the training vocabulary. We
evaluate the model on conventional language modeling as well as challenging
cross-domain settings with an open vocabulary, finding that it matches or
outperforms previous state-of-the-art output embedding methods and adaptation
approaches. Our analysis attributes the improvements to sample efficiency: our
model is more accurate for low-frequency words.

本研究提出了基于结构化词典的完全组成的输出嵌入层，用于改善语言模型在新域和任务中的适应能力问题，为我们所知，这是第一个单词级别的语言模型，其大小不取决于训练词汇表，并通过低频词的样本效率来提高精度。