Transformers for Headline Selection for Russian News Clusters
Pavel Voropaev, Olga Sopilnyak · Computational Linguistics and Intellectual Technologies · 2021
In this paper, we explore various multilingual and Russian pre-trained transformer-based models for the Dialogue Evaluation 2021 shared task on headline selection.Our experiments show that the combined approach is superior to individual multilingual and monolingual models.We present an analysis of a number of ways to obtain sentence embeddings and learn a ranking model on top of them.We achieve the result of 87.28% and 86.60% accuracy for the public and private test sets respectively.