Exploring Multiple Strategies to Improve Multilingual Coreference Resolution in CorefUD

Coreference resolution is the task of identifying language expressions that refer to the same real-world entity (antecedent) within a text. These coreferential expressions can sometimes appear within a single sentence, but often, they are spread across multiple sentences. In some challenging cases, it is necessary to consider the entire document to determine whether two expressions refer to the same entity. The task can be divided into two main subtasks: identifying entity mentions and grouping these mentions based on the real-world entities they refer to. Coreference resolution is closely related to anaphora resolution, as discussed in [2] Historically, coreference resolution was a standard preprocessing step in various natural language processing (NLP) tasks, such as machine translation, summarization, and information extraction. Although recent large language models have achieved state-of-the-art results in coreference resolution, they are expensive to train and deploy, and traditional (discriminative) approaches remain competitive. Expressing this task in natural language is challenging, and to the best of our knowledge, there have been no successful attempts to utilize large chatbots (like ChatGPT-4) to achieve superior results. Coreference resolution becomes particularly challenging in low-resource languages. One strategy to address this challenge is to train a multilingual model on datasets from multiple languages, thereby transferring knowledge from resource-rich languages to those with fewer resources.