Coreference resolution is an important task in natural language processing, since it can be applied to such vital tasks as information retrieval, text summarization, question answering, sentiment analysis and machine translation. In this paper, we present a study on the effectiveness of several approaches to coreference resolution, focusing on the RuCoCo dataset as well as results of participation in the Dialogue Evaluation 2023. We explore ways to increase the dataset size by using pseudo-labelling and data translated from another language. Using such technics we managed to triple the size of dataset, make it more diverse and improve performance of autoregressive structured prediction (ASP) on coreference resolution task. This approach allowed us to achieve the best results on RuCoCo private test with increase of F1-score by 1.8, Precision by 0.5 and Recall by 3.0 points compared to the second-best leaderboard score. Our results demonstrate the potential of the ASP model and the importance of utilizing diverse training data for coreference resolution.
