Measuring bias is key for better understanding and addressing unfairness in NLP/ML
models. This is often done via fairness metrics, which quantify the differences in a model's …

Real world data differs radically from the benchmark corpora we use in natural language
processing (NLP). As soon as we apply our technologies to the real world, performance …

It is often stated that human languages, as other biological systems, are shaped by cost-
cutting pressures but, to what extent? Attempts to quantify the degree of optimality of …

To approximately parse an unfamiliar language, it helps to have a treebank of a similar
language. But what if the closest available treebank still has the wrong word order? We …

Identifying factors that make certain languages harder to model than others is essential to
reach language equality in future Natural Language Processing technologies. Free-order …

I explore the hypothesis that the universal properties of human languages can be explained
in terms of efficient communication given fixed human information processing constraints. I …

The new and growing field of Quantitative Dependency Syntax has emerged at the
crossroads between Dependency Syntax and Quantitative Linguistics. One of the main …

Universal Dependency (UD) annotations, despite their usefulness for cross-lingual tasks
and semantic applications, are not optimised for statistical parsing. In the paper, we ask what …

Despite their practical success and impressive performances, neural-network-based and
distributed semantics techniques have often been criticized as they remain fundamentally …

We used the Potsdam-Allahabad Hindi eye-tracking corpus to investigate the role of
wordlevel and sentence-level factors during sentence comprehension in Hindi. Extending …