Multi‐task learning with contextual hierarchical attention for Korean coreference resolution

Abstract

AbstractCoreference resolution is a task in discourse analysis that links several headwords used in any document object. We suggest pointer networks‐based coreference resolution for Korean using multi‐task learning (MTL) with an attention mechanism for a hierarchical structure. As Korean is a head‐final language, the head can easily be found. Our model learns the distribution by referring to the same entity position and utilizes a pointer network to conduct coreference resolution depending on the input headword. As the input is a document, the input sequence is very long. Thus, the core idea is to learn the word‐ and sentence‐level distributions in parallel with MTL, while using a shared representation to address the long sequence problem. The suggested technique is used to generate word representations for Korean based on contextual information using pre‐trained language models for Korean. In the same experimental conditions, our model performed roughly 1.8% better on CoNLL F1 than previous research without hierarchical structure.