N ovel CR : A Large-Scale Bilingual Dataset Tailored for Long-Span Coreference Resolution (2025)

Abstract: Coreference resolution (CR) endeavors to match pronouns, noun phrases, etc. with their referent entities, acting as an important step for deep text understanding. Presently available CR datasets are either small in scale or restrict coreference resolution to a limited text span. In this paper, we present NovelCR, a large-scale bilingual benchmark designed for long-span coreference resolution. NovelCR features extensive annotations, including 148k mentions in NovelCR-en and 311k mentions in NovelCR-zh. Moreover, the dataset is notably rich in long-span coreference pairs, with 85% of pairs in NovelCR-en and 83% in NovelCR-zh spanning across three or more sentences. Experiments on NovelCR reveal a large gap between state-of-the-art baselines and human performance, highlighting that NovelCR remains an open issue.

## introduction coreference resolution (cr) aims to identify mentions and their referent entities from text. for instance, given the sentence "recently, apple sued qualcomm, suing it for failing to cooperate by contracts", coreference resolution needs to distinguish that mention it here refers to entity qualcomm instead of apple. coreference resolution is a core task in deep text analysis and acts as a prerequisite for multiple advanced natural language processing applications such as machine reading comprehension @xcite , information extraction @xcite , and multi-round dialogue construction @xcite . however, existing coreference resolution datasets either suffer from small data scales or restrict coreference resolution within a limited text span. ace2004 @xcite annotates coreferences from merely 451 documents. the data scales of wikicoref @xcite , @xcite , muc-7 @xcite , stm-coref @xcite are even smaller, containing 30, 60, 50, and 110 documents, respectively. given their small scale, none of these coreference datasets can fairly assess the performance of modern neural coreference resolution models. the winograd schema challenge (wsc) corpus @xcite restricts referential ambiguity to intra-sentential settings, so all anaphoric links are resolved within a single sentence. other public benchmarks likewise exhibit only modest cross-sentence span lengths. the average sentence distance between antecedent and anaphor in conll-2012 @xcite , ecb+ @xcite , gap @xcite , longtonotes @xcite , and dwie @xcite ) is just 2.9, 3.1, 2.3, 3.3, and 2.8 sentences, respectively. the predominance of short-span coreference pairs in these datasets substantially lowers the overall difficulty of the task these datasets pose. in this paper, we introduce novelcr, a largescale bilingual benchmark designed to tackle longspan coreference resolution. long-span coreference resolution requires models to reason over broad discourse context rather than local sentence cues. as figure 1 shows, linking the lady on the ground back to jerebal demands that the models track dialogue-role shifts across several sentences, ignore intervening mentions, and overcome an apparent gender mismatch -highlighting the depth of contextual understanding required for robust coreference resolution. to ensure that novelcr captures such complex phenomena, we focus on annotating the coreference chains of novel characters, whose mentions are naturally dispersed across extended narrative spans. for example, characters like jerebal, quila, and quil in figure 1 (a) are frequently referenced after multiple intervening sentences, making them ideal anchors for evaluating discourse-level resolution capabilities. ## dataset construction in this section, we illustrate the dataset construction process. as shown in figure 2 , we construct novelcr in three steps: novel chapter collection, mention detection, and coreference identification. novel chapter collection aims to gather chapters from a wide range of genres sourced from online novel websites. mention detection leverages ner tools (stanford corenlp tool for english, ltp tool for chinese) and prompt learning to mine potential entities and mentions from novel chapters. coreference identification uses crowdsourcing to distinguish coreference pairs in chapters by converting coreference resolution into multiple-choice questions. ## conclusion we propose novelcr, a large-scale bilingual benchmark designed for long-span coreference resolution. novelcr features a substantial dataset size and contains numerous lengthy coreferences. extensive experiments on novelcr demonstrate that the performance of the state-of-the-art baselines cannot catch up with human beings, showing that novelcr remains an unresolved challenge.