The L ou Dataset - Exploring the Impact of Gender-Fair Language in G erman Text Classification (2024)

Abstract: Gender-fair language, an evolving linguistic variation in German, fosters inclusion by addressing all genders or using neutral forms. However, there is a notable lack of resources to assess the impact of this language shift on language models (LMs) might not been trained on examples of this variation. Addressing this gap, we present Lou, the first dataset providing high-quality reformulations for German text classification covering seven tasks, like stance detection and toxicity classification. We evaluate 16 mono- and multi-lingual LMs and find substantial label flips, reduced prediction certainty, and significantly altered attention patterns. However, existing evaluations remain valid, as LM rankings are consistent across original and reformulated instances. Our study provides initial insights into the impact of gender-fair language on classification for German. However, these findings are likely transferable to other languages, as we found consistent patterns in multi-lingual and English LMs.

## introduction language is constantly evolving. this change involves dialect variations of specific localities or the slang of certain generations @xcite . such linguistic variations reflect societal changes, where negotiations emerge and influence how individuals speak. notable shifts are gender-fair formulations in lan-figure 1 : a german stance detection instance from the lou dataset. we reformulate the masculine formulation konsumenten (consumers) regarding six inclusive or neutral strategies, highlighted in yellow. translation: consumers must be well supported. as language models (lms) are inherently biased from training on data from the past @xcite @xcite , current research increasingly addresses the question 'how does gender-fair language impact lms?'. this includes examining the gender bias of machine translation regarding gender-fair language @xcite and pronouns @xcite , or fundamentally concentrate on lms' limitations when interpreting pronouns @xcite @xcite . despite the widespread application of text classification @xcite , there is a notable lack of resources and scientific effort to examine the impact of gender-fair language on classification systems. consequently, it remains unclear whether lms perform consistently without unwanted side effects when processing gender-fair language under the inclusive aim. to address this research gap, we introduce lou ( § 3), a german dataset featuring high-quality text reformulations following six reformulation strategies (figure 1 ). while creating these reformulations, we also examine the reliability of amateurs with moderate experience using gender-fair language compared to professionals with linguistic backgrounds. using the resulting 3.6k reformulated instances from seven classification tasks, we systematically evaluate the impact of gender-fair language and specific reformulation strategies for fine-tuning and zero-shot classification setups covering 16 lms ( § 4). we then compare these results with the original instances ( § 5 and § 6) to address four key research questions. rq1: do amateurs produce gender-fair reformulations with sufficient quality? no. amateurs struggle to consistently apply reformulation strategies, with an error rate of up to 31% for genderstern, hinting at a lack of societal establishment and standardization. rq2: does gender-fair language impact german text classification during inference? yes. gender-fair language leads to task performance variations in macro f 1 score, ranging from -1.0 to +4.0, and flips up to 10.9% of individual predictions. however, the effects of distinct reformulation strategies vary. those making minimal sentence adjustments, such as genderstern, tend to enhance performance. at the same time, neutralizationfocused strategies, like de-e or neutral, which substantially alter the text, generally lead to lower performance. rq3: do lms process gender-fair language differently? yes. gender-fair language notably impacts how the lower lm layers process reformulated instances compared to the original ones. further, altered attention patterns and decreased prediction certainty lead to the observed label flips. rq4: what are the practical implications of encountering gender-fair language? existing datasets and evaluations remain valid (consistent lm rankings), but significant label flips occur, especially in tasks with lower absolute performance. observing mostly syntactic and consistent effects across german, english, and multi-lingual lms, our findings are relevant to other languages with similar reformulation strategies, such as using the interpoint (•) as an inclusion character in french. ## the lou dataset lou marks the largest collection of reformulated instances for german text classification. with 3.6k reformulations following six reformulation strategies, lou enables thoroughly assessing the impact of gender-fair language and the individual strategies across seven classification tasks. in the following, we discuss the used data ( § 3.1) before focusing on the reformulation study ( § 3.2). ## results we discuss results obtained across the seven lou tasks. first, we establish our baseline with results on the original samples (i). next, we focus on rq2 and the substantial impact of gender-fair language on aggregated evaluation (ii, iii) and individual predictions (iv). addressing rq4, we confirm that existing datasets and evaluations retain their validity under gender-fair language (v). ## analysis focusing on rq3, we discuss the pronounced effect of reformulations on lower model layers (i) and find reformulations significantly alter attention patterns and decrease prediction certainty (ii), and these properties are crucial for label flips (iii). next, we qualitatively analyze and show in figure 6 that the six strategies are better distinguishable on lower layers by projecting r for all layers to 1d using t-sne. while these plots focus on gbert-base only, we observe similar patterns for other lms (appendix § a.10).doppelnennung, neutral, and de-e are more different, while strategies using gender character (genderstern, gendergap, and genderdoppelpunkt) overlap. noteworthy, the specific special characters are again crucial, genderdoppelpunkt (:) differs from genderstern (*) and gendergap (_). these insights confirm again that the impact of gender-fair language is primarily syntactic. flip correct overall no yes no yes norm. flesch -2.01 -2.00 -2.3 -2.08 -1.44 norm. length +1.73 +1.74 +1.69 +1.76 +1.42 prediction certainty -0.14 +0.35 -10.0 +0.49 -2.03 attention max +0.41 +0.37 +1.14 +0.33 +0.56 attention variation +0.10 +0.08 +0.23 +0.07 +0.13 figure 7 shows the relation between the different properties and the label flip fraction in more detail. while the flesch score shows less pronounced effects, the flip fraction tends to be higher (up to 6%) for shorter instances. further, the flip fraction is crucially higher when an lm predicts with less certainty (up to 15%) and exhibits a high attention maximum or variation, up to 15% and 20%). ## related work previous work shows lms embodying substantially stereotypical bias @xcite @xcite regarding gender, profession, race, and religion. in particular, gender bias gained more attention recently @xcite . while some works focus on analyzing the existence of gender bias @xcite @xcite , others aim to reduce this bias @xcite @xcite . another line of work examines the effects of gender-fair language, such as how lms process (neo)pronouns @xcite @xcite . gender-fair language is also broadly studied in translation @xcite , focusing on the effects of gender bias @xcite . this includes analyzing the acceptance of gender-fair formulations (attanasio et al., 2023), gender neutralization @xcite , the use of interpretability methods @xcite , and the impact of pronouns on translation @xcite . unlike previous work, we focus on the impact of gender-fair language on classification inference. specifically, we present with lou the first dataset of its kind, to assess the impact of genderfair language on text classification regarding seven german tasks and analyze this impact in detail. ## conclusion we comprehensively assess the impact of genderfair language on german text classification tasks. specifically, we introduce lou, a high-quality dataset of parallel annotated reformulations that employ various gender-fair strategies. our systematic evaluation and analysis reveal that aggregated evaluations of original data maintain their validity under gender-fair language. however, absolute performance tends to increase, while predicted labels can flip with a probability of up to 10.9%, particularly due to significantly reduced prediction certainty and altered attention patterns. these findings highlight the importance of considering this linguistic variation, especially since even minor syntactic changes can critically alter how lms process individual instances. moving forward, we plan to extend lou to other languages that employ similar gender-fair reformulation strategies, such as italian and french, and work on adopting lms for this linguistic variation.