Combining multiple source embeddings to create meta-embeddings is considered effective to obtain more accurate embeddings. Different methods have been proposed to develop meta-embeddings from a given set of source embeddings. However, the source embeddings can contain unfair gender bias, and the bias in the combination of multiple embeddings and debiasing it effectively have not been studied yet. In this paper, we investigate the bias in three types of meta-embeddings: (1) Multi-Source No-Debiasing: meta-embedding from multiple source embeddings without any debiasing. The experimental results show that meta-embedding amplifies the gender bias compared to those of input source embeddings; (2) Multi-Source Single-Debiasing: meta-embedding from multiple source embeddings debiased by a single method and it can be created in three ways: debiasing each source embedding, debiasing the learned meta-embeddings, and debiasing both source embeddings and meta-embeddings. The results show that debiasing both is the best in two out of three bias evaluation benchmarks; (3) Single-Source Multi-Debiasing: meta-embedding from the same source embedding debiased by different methods. It performed more effectively than its source embeddings debiased with a single method in all three bias evaluation benchmarks.
翻译:将多源嵌入合并在一起以创建元构件被认为有效,以获得更准确的嵌入。已经提议了不同的方法来开发来自特定源嵌入的元组。但是,源嵌入可能包含不公平的性别偏向,而将多个嵌入和贬低的混合在一起的偏向尚未研究。在本文中,我们调查三种元构的偏向:(1) 多源不偏向:从多个源嵌入中从多个源嵌入并没有任何偏移。实验结果显示,从一个源嵌入到一个源嵌入和有效降低其偏向的混合。我们调查三种元嵌入的偏向:(1) 多源不偏向:从多个源嵌入到不偏向:从多个源嵌入,从多个源嵌入到不偏移; 实验结果显示,与输入输入源相比,元嵌入的性别偏移扩大了性别偏向;(2) 多源单源单一嵌入的单一嵌入,从三个源的递解位化方式显示,从三个源的递减。