Indigenous Language Revitalization and the Dilemma of Gender Bias

Oussama Hansal, N. Le, F. Sadat
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引用次数: 3

Abstract

Natural Language Processing (NLP), through its several applications, has been considered as one of the most valuable field in interdisciplinary researches, as well as in computer science. However, it is not without its flaws. One of the most common flaws is bias. This paper examines the main linguistic challenges of Inuktitut, an indigenous language of Canada, and focuses on gender bias identification and mitigation. We explore the unique characteristics of this language to help us understand the right techniques that can be used to identify and mitigate implicit biases. We use some methods to quantify the gender bias existing in Inuktitut word embeddings; then we proceed to mitigate the bias and evaluate the performance of the debiased embeddings. Next, we explain how approaches for detecting and reducing bias in English embeddings may be transferred to Inuktitut embeddings by properly taking into account the language’s particular characteristics. Next, we compare the effect of the debiasing techniques on Inuktitut and English. Finally, we highlight some future research directions which will further help to push the boundaries.
本土语言复兴与性别偏见困境
自然语言处理(NLP)通过其多种应用,已被认为是跨学科研究和计算机科学中最有价值的领域之一。然而,它并非没有缺点。最常见的缺陷之一是偏见。本文考察了加拿大土著语言因纽特语的主要语言挑战,并着重于性别偏见的识别和缓解。我们探索这种语言的独特特征,以帮助我们理解可以用来识别和减轻隐性偏见的正确技术。我们使用一些方法来量化因纽特语词嵌入中存在的性别偏见;然后我们继续减轻偏差并评估去偏差嵌入的性能。接下来,我们解释了如何通过适当考虑语言的特殊特征,将检测和减少英语嵌入中的偏见的方法转移到因纽特语嵌入中。接下来,我们比较了去偏技术对因纽特语和英语的影响。最后,我们强调了未来的研究方向,这将有助于进一步推动边界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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