Existing approaches to mapping-based cross-lingual word embeddings are based
on the assumption that the source and target embedding spaces are structurally
similar. The structures of embedding spaces largely depend on the co-occurrence
statistics of each word, which the choice of context window determines. Despite
this obvious connection between the context window and mapping-based
cross-lingual embeddings, their relationship has been underexplored in prior
work. In this work, we provide a thorough evaluation, in various languages,
domains, and tasks, of bilingual embeddings trained with different context
windows. The highlight of our findings is that increasing the size of both the
source and target window sizes improves the performance of bilingual lexicon
induction, especially the performance on frequent nouns.

本研究系统评估了使用不同上下文窗口大小训练的跨语言词嵌入在多种语言、领域和任务中的性能，并发现增加源和目标词窗口大小可以提高双语词汇归纳的性能，尤其是对于频繁的名词。

重温上下文窗口：用于跨语言词嵌入的方法

Revisiting the Context Window for Cross-lingual Word Embeddings

Recent studies have shown that word embeddings exhibit gender bias inherited
from the training corpora. However, most studies to date have focused on
quantifying and mitigating such bias only in English. These analyses cannot be
directly extended to languages that exhibit morphological agreement on gender,
such as Spanish and French. In this paper, we propose new metrics for
evaluating gender bias in word embeddings of these languages and further
demonstrate evidence of gender bias in bilingual embeddings which align these
languages with English. Finally, we extend an existing approach to mitigate
gender bias in word embeddings under both monolingual and bilingual settings.
Experiments on modified Word Embedding Association Test, word similarity, word
translation, and word pair translation tasks show that the proposed approaches
effectively reduce the gender bias while preserving the utility of the
embeddings.

该研究提出了一种新的度量标准来评估西班牙语和法语等语言中的词嵌入性别偏见，并进一步证明了双语词嵌入与英文词嵌入存在性别偏见的一致性。同时作者还提供了一种新的方法用于缓解这种偏见。实验结果表明这些方法有效地减少了性别偏见，同时也保持了嵌入向量的实用性。