arXiv subjects

Maria Symeonaki

Publications and source records attributed to Maria Symeonaki.

3 recordsLinked to original sources

arXiv2025

Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Terms

Machine Translation (MT) systems frequently encounter gender-ambiguous occupational terms, where they must assign gender without explicit contextual cues. While individual translations in such cases may not be inherently biased, systematic patterns-such as consistently translating certain professions with specific genders-can emerge, reflecting and perpetuating societal stereotypes. This ambiguity challenges traditional instance-level single-answer evaluation approaches, as no single gold standard translation exists. To address this, we introduce GRAPE, a probability-based metric designed to evaluate gender bias by analyzing aggregated model responses. Alongside this, we present GAMBIT, a benchmarking dataset in English with gender-ambiguous occupational terms. Using GRAPE, we evaluate several MT systems and examine whether their gendered translations in Greek and French align with or diverge from societal stereotypes, real-world occupational gender distributions, and normative standards

cs.CL↗

arXiv2024

GOSt-MT: A Knowledge Graph for Occupation-related Gender Biases in Machine Translation

Gender bias in machine translation (MT) systems poses significant challenges that often result in the reinforcement of harmful stereotypes. Especially in the labour domain where frequently occupations are inaccurately associated with specific genders, such biases perpetuate traditional gender stereotypes with a significant impact on society. Addressing these issues is crucial for ensuring equitable and accurate MT systems. This paper introduces a novel approach to studying occupation-related gender bias through the creation of the GOSt-MT (Gender and Occupation Statistics for Machine Translation) Knowledge Graph. GOSt-MT integrates comprehensive gender statistics from real-world labour data and textual corpora used in MT training. This Knowledge Graph allows for a detailed analysis of gender bias across English, French, and Greek, facilitating the identification of persistent stereotypes and areas requiring intervention. By providing a structured framework for understanding how occupations are gendered in both labour markets and MT systems, GOSt-MT contributes to efforts aimed at making MT systems more equitable and reducing gender biases in automated translations.

cs.CL↗

arXiv2024

Examining the development of attitude scales using Large Language Models (LLMs)

For nearly a century, social researchers and psychologists have debated the efficacy of psychometric scales for attitude measurement, focusing on Thurstone's equal appearing interval scales and Likert's summated rating scales. Thurstone scales fell out of favour due to the labour intensive process of gathering judges' opinions on the initial items. However, advancements in technology have mitigated these challenges, nullifying the simplicity advantage of Likert scales, which have their own methodological issues. This study explores a methodological experiment to develop a Thurstone scale for assessing attitudes towards individuals living with AIDS. An electronic questionnaire was distributed to a group of judges, including undergraduate, postgraduate, and PhD students from disciplines such as social policy, law, medicine, and computer engineering, alongside established social researchers, and their responses were statistically analysed. The primary innovation of this study is the incorporation of an Artificial Intelligence (AI) Large Language Model (LLM) to evaluate the initial 63 items, comparing its assessments with those of the human judges. Interestingly, the AI provided also detailed explanations for its categorisation. Results showed no significant difference between AI and human judges for 35 items, minor differences for 23 items, and major differences for 5 items. This experiment demonstrates the potential of integrating AI with traditional psychometric methods to enhance the development of attitude measurement scales.

stat.AP↗