4 citations · 6 across the 10 of their papers we have counts for
11 papers
emb-diversity: A Tool for Embedding-Based Measurement of Data Diversity
Cantao Su, Menan Velayuthan, Esther Ploeger +2
There is growing evidence that data diversity is crucial for developing fair and robust NLP models. However, current approaches to measure diversity remain inconsistent and fragmen…
We Need to Measure Data Diversity in NLP -- Better and Broader
Dong Nguyen, Esther Ploeger
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 m…
Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation
Huiyuan Lai, Esther Ploeger, Rik van Noord +1
Neural machine translation (NMT) systems amplify lexical biases present in their training data, leading to artificially impoverished language in output translations. These language…
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
Angelika Romanou, Negar Foroutan, Anna Sotnikova +56
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal val…
How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP
Kushal Tatariya, Artur Kulmizev, Wessel Poelman +6
Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in NLP. However, in recent years, such assumptions of high quality have…
Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation
Esther Ploeger, Huiyuan Lai, Rik van Noord +1
Machine translations are found to be lexically poorer than human translations. The loss of lexical diversity through MT poses an issue in the automatic translation of literature, w…
A Principled Framework for Evaluating on Typologically Diverse Languages
Esther Ploeger, Wessel Poelman, Andreas Holck Høeg-Petersen +3
Beyond individual languages, multilingual natural language processing (NLP) research increasingly aims to develop models that perform well across languages generally. However, eval…
Multilingual Gradient Word-Order Typology from Universal Dependencies
Emi Baylor, Esther Ploeger, Johannes Bjerva
While information from the field of linguistic typology has the potential to improve performance on NLP tasks, reliable typological data is a prerequisite. Existing typological dat…
What is "Typological Diversity" in NLP?
Esther Ploeger, Wessel Poelman, Miryam de Lhoneux +1
The NLP research community has devoted increased attention to languages beyond English, resulting in considerable improvements for multilingual NLP. However, these improvements onl…
The Past, Present, and Future of Typological Databases in NLP
Emi Baylor, Esther Ploeger, Johannes Bjerva
Typological information has the potential to be beneficial in the development of NLP models, particularly for low-resource languages. Unfortunately, current large-scale typological…
CreoleVal: Multilingual Multitask Benchmarks for Creoles
Heather Lent, Kushal Tatariya, Raj Dabre +18
Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research.While the genealogical ties between Creoles and a number of h…