no code implementations • • Jakub Piskorski, Bogdan Babych, Zara Kancheva, Olga Kanishcheva, Maria Lebedeva, Michał Marcińczuk, Preslav Nakov, Petya Osenova, Lidia Pivovarova, Senja Pollak, Pavel Přibáň, Ivaylo Radev, Marko Robnik-Sikonja, Vasyl Starko, Josef Steinberger, Roman Yangarber
Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task.
Parliamentary and legislative debate transcripts provide an exciting insight into elected politicians' opinions, positions, and policy preferences.
To alleviate the problem, we propose two semi-supervised methods to guide the annotation process: a Bayesian deep learning model and a Bayesian ensemble method.
Our approach not only improves the classification performance of the state-of-the-art multilingual BERT model but the computed reliability scores also significantly reduce the workload in an inspection of ofending cases and reannotation campaigns.
The performance of cross-lingual models obtained with the multilingual BERT and LASER library is comparable, and the differences are language-dependent.