Sense embedding learning methods learn multiple vectors for a given ambiguous
word, corresponding to its different word senses. For this purpose, different
methods have been proposed in prior work on sense embedding learning that use
different sense inventories, sense-tagged corpora and learning methods.
However, not all existing sense embeddings cover all senses of ambiguous words
equally well due to the discrepancies in their training resources. To address
this problem, we propose the first-ever meta-sense embedding method --
Neighbour Preserving Meta-Sense Embeddings, which learns meta-sense embeddings
by combining multiple independently trained source sense embeddings such that
the sense neighbourhoods computed from the source embeddings are preserved in
the meta-embedding space. Our proposed method can combine source sense
embeddings that cover different sets of word senses. Experimental results on
Word Sense Disambiguation (WSD) and Word-in-Context (WiC) tasks show that the
proposed meta-sense embedding method consistently outperforms several
competitive baselines.

本文提出了第一种元语义嵌入方法 - 近邻保留元语义嵌入，该方法通过组合多个独立训练的源感知嵌入来学习元感知嵌入，从而保留了源嵌入中计算的感知邻域，实验证明该方法在词义消歧和上下文词任务中持续优于竞争基线。

联合学习元感嵌入：从预训练静态感觉嵌入中学习

Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained  Static Sense Embeddings

Sense embedding learning methods learn different embeddings for the different
senses of an ambiguous word. One sense of an ambiguous word might be socially
biased while its other senses remain unbiased. In comparison to the numerous
prior work evaluating the social biases in pretrained word embeddings, the
biases in sense embeddings have been relatively understudied. We create a
benchmark dataset for evaluating the social biases in sense embeddings and
propose novel sense-specific bias evaluation measures. We conduct an extensive
evaluation of multiple static and contextualised sense embeddings for various
types of social biases using the proposed measures. Our experimental results
show that even in cases where no biases are found at word-level, there still
exist worrying levels of social biases at sense-level, which are often ignored
by the word-level bias evaluation measures.

本文提出一种评估感觉嵌入社会偏见的基准数据集和新的感知特定偏见评估指标，并对多个静态和上下文感知嵌入进行广泛评估，发现即使在单词级别没有发现偏见的情况下，仍存在令人担忧的感知级别的社会偏见，这通常被单词级别偏见评估措施所忽略。