Anton Ragni


HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models
Yizhi Li | Ge Zhang | Bohao Yang | Chenghua Lin | Anton Ragni | Shi Wang | Jie Fu
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022

Fairness has become a trending topic in natural language processing (NLP) and covers biases targeting certain social groups such as genders and religions. Yet regional bias, another long-standing global discrimination problem, remains unexplored still. Consequently, we intend to provide a study to analyse the regional bias learned by the pre-trained language models (LMs) that are broadly used in NLP tasks. While verifying the existence of regional bias in LMs, we find that the biases on regional groups can be largely affected by the corresponding geographical clustering. We accordingly propose a hierarchical regional bias evaluation method (HERB) utilising the information from the sub-region clusters to quantify the bias in the pre-trained LMs. Experiments show that our hierarchical metric can effectively evaluate the regional bias with regard to comprehensive topics and measure the potential regional bias that can be propagated to downstream tasks. Our codes are available at


Incorporating Uncertainty into Deep Learning for Spoken Language Assessment
Andrey Malinin | Anton Ragni | Kate Knill | Mark Gales
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

There is a growing demand for automatic assessment of spoken English proficiency. These systems need to handle large variations in input data owing to the wide range of candidate skill levels and L1s, and errors from ASR. Some candidates will be a poor match to the training data set, undermining the validity of the predicted grade. For high stakes tests it is essential for such systems not only to grade well, but also to provide a measure of their uncertainty in their predictions, enabling rejection to human graders. Previous work examined Gaussian Process (GP) graders which, though successful, do not scale well with large data sets. Deep Neural Network (DNN) may also be used to provide uncertainty using Monte-Carlo Dropout (MCD). This paper proposes a novel method to yield uncertainty and compares it to GPs and DNNs with MCD. The proposed approach explicitly teaches a DNN to have low uncertainty on training data and high uncertainty on generated artificial data. On experiments conducted on data from the Business Language Testing Service (BULATS), the proposed approach is found to outperform GPs and DNNs with MCD in uncertainty-based rejection whilst achieving comparable grading performance.


Initial Experiments with Estonian Speech Recognition
Anton Ragni
Proceedings of the 16th Nordic Conference of Computational Linguistics (NODALIDA 2007)