Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology (1906.04571v3)

Abstract: Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender stereotypes in English, the approaches that are commonly employed produce ungrammatical sentences in morphologically rich languages. We present a novel approach for converting between masculine-inflected and feminine-inflected sentences in such languages. For Spanish and Hebrew, our approach achieves F1 scores of 82% and 73% at the level of tags and accuracies of 90% and 87% at the level of forms. By evaluating our approach using four different languages, we show that, on average, it reduces gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality.

• The paper introduces a novel counterfactual data augmentation method employing a Markov Random Field to ensure grammatical gender agreement in morphologically rich languages.
• It details a four-step process for sentence transformation that maintains syntactic integrity while mitigating gender stereotypes.
• Experimental results demonstrate up to a 2.5-fold reduction in gender bias with high accuracy in tag-level and form-level evaluations across languages like Spanish and Hebrew.

Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Morphologically Rich Languages

The prevalence of gender stereotypes in NLP systems is a concern due to their potential to perpetuate gender biases present in underlying language corpora. Existing efforts to mitigate such biases have predominantly focused on English, a language with comparatively minimal morphological inflection. This paper addresses this issue for morphologically rich languages, which pose unique challenges due to their complex morphological structures that require gender agreement.

The researchers propose a novel counterfactual data augmentation (CDA) method designed specifically for morphologically rich languages. Their approach considers languages where words are gender-inflected, like Spanish and Hebrew, which requires consistent gender agreement across nouns, adjectives, and verbs within a sentence. Common techniques that work for English, such as simple swapping of gendered words, fail here due to their inability to maintain grammatical correctness, thus underscoring the need for this innovative methodology.

Contributions and Methodology

1. Markov Random Field Modeling: The paper introduces a Markov Random Field (MRF) framework to effectively infer the necessary changes in a sentence when the gender of a noun is altered. This MRF uses dependency trees, POS tags, and morpho-syntactic tags to ensure accurate morpho-syntactic agreement, a critical aspect for maintaining grammaticality.
2. Four-Step Process for Sentence Transformation:

The approach involves: - Analyzing the sentence structure. - Identifying and altering the target gendered noun. - Inferring updated morpho-syntactic tags using the MRF. - Reinflecting the words based on the new gender information.

1. Practical Application and Results: Implementing this method across four languages, the experiments demonstrate significant reductions in gender stereotyping—up to a factor of 2.5—while successfully maintaining grammatical integrity. In intrinsic evaluations on Hebrew and Spanish, tag-level $F_1$ scores of 73% and 82% and form-level accuracies of 87% and 90% are achieved, respectively.

Implications and Future Work

The paper has practical implications in reducing gender bias in NLP systems, particularly in applications like automated approval processes for hiring, where gender bias can have adverse societal impacts. Theoretically, it advances the design and training of NLP models by integrating gender fairness within systems managing syntactically and morphologically complex languages.

Looking forward, there are prospects for further refining the model, such as incorporating co-reference information to handle sentences with multiple animate noun references accurately. Expanding annotated resources for other morphologically rich languages could enhance model training and evaluation.

The execution of such frameworks in practical NLP systems can potentially influence diverse applications by promoting inclusivity and fairness, suggesting a promising direction for future research in the domain of fair AI and linguistically diverse NLP models.