Esther Ploeger — accepted AI-conference papers

2025

INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge

Angelika Romanou, Negar Foroutan, Anna Sotnikova, Sree Harsha Nelaturu, Shivalika Singh, Rishabh Maheshwary +51

ICLR 2025spotlight

The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the development of functional LLMs in many languages (i.e., mult…

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2025

Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation

Huiyuan Lai, Esther Ploeger, Rik Van Noord, Antonio Toral

ACL 2025long

Neural machine translation (NMT) systems amplify lexical biases present in their training data, leading to artificially impoverished language in output translations. These language-level characteristics render automatic translations different from text originally written in a language and human tran…

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2025

SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models

Margaret Mitchell, Giuseppe Attanasio, Ioana Baldini, Miruna Clinciu, Jordan Clive, Pieter Delobelle +48

NAACL 2025long

Large Language Models (LLMs) reproduce and exacerbate the social biases present in their training data, and resources to quantify this issue are limited. While research has attempted to identify and mitigate such biases, most efforts have been concentrated around English, lagging the rapid advanceme…

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2025

We Need to Measure Data Diversity in NLP — Better and Broader

Dong Nguyen, Esther Ploeger

EMNLP 2025

Although diversity in NLP datasets has received growing attention, the question of how to measure it remains largely underexplored. This opinion paper examines the conceptual and methodological challenges of measuring data diversity and argues that interdisciplinary perspectives are essential for de

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2024

What is “Typological Diversity” in NLP?

Esther Ploeger, Wessel Poelman, Miryam de Lhoneux, Johannes Bjerva

EMNLP 2024main

The NLP research community has devoted increased attention to languages beyond English, resulting in considerable improvements for multilingual NLP. However, these improvements only apply to a small subset of the world’s languages. An increasing number of papers aspires to enhance generalizable mult…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2023

The Past, Present, and Future of Typological Databases in NLP

Emi Baylor, Esther Ploeger, Johannes Bjerva

EMNLP 2023short findings

Typological information has the potential to be beneficial in the development of NLP models, particularly for low-resource languages. Unfortunately, current large-scale typological databases, notably WALS and Grambank, are inconsistent both with each other and with other sources of typological info…

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