LongtoNotes: OntoNotes with Longer Coreference ChainsDownload PDF

Anonymous

16 Feb 2022 (modified: 05 May 2023)ACL ARR 2022 February Blind SubmissionReaders: Everyone
Abstract: Ontonotes has served as the most important benchmark for coreference resolution. However, for ease of annotation, several long documents in Ontonotes were split into smaller parts. In this work, we build a corpus of coreference-annotated documents of significantly longer length than what is currently available. We do so by providing an accurate, manually-curated, merging of annotations from documents that were split into multiple parts in the original Ontonotes annotation process. The resulting corpus, which we call LongtoNotes contains documents in multiple domains of the English language with varying lengths, the longest of which are up to 8x the length of documents in Ontonotes, and 2x those in Litbank. We evaluate state-of-the-art neural coreference systems on this new corpus, analyze the relationships between model architectures/ hyperparameters and document length on performance and efficiency of the models, and demonstrate areas of improvement in long document coreference modelling revealed by our new corpus.
Paper Type: long
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