Papers by Giuliano Martinelli
BOOKCOREF: Coreference Resolution at Book Scale (2025.acl-long)
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| Challenge: | Existing benchmarks for coreference resolution systems are limited in length and do not adequately assess system capabilities at the book scale. |
| Approach: | They propose a novel pipeline that produces high-quality coreference resolution annotations on full narrative texts and a book-scale benchmark, BOOKCOREF. |
| Outcome: | The proposed pipeline produces high-quality coreference resolution annotations on full texts with an average document length of more than 200,000 tokens. |
CNER: Concept and Named Entity Recognition (2024.naacl-long)
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| Challenge: | Concept and Named Entity Recognition (CNER) is a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Approach: | They propose a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Outcome: | The proposed task gains +5.4 and +8 macro F1 points when performed as a unified task compared to specialized named entity and concept recognition systems. |
xCoRe: Cross-context Coreference Resolution (2025.emnlp-main)
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| Challenge: | Current coreference resolution systems are limited to short-to-medium-sized documents and struggle to scale to very long documents due to architectural limitations and implied memory costs. |
| Approach: | They propose a unified approach to coreference resolution that unifies two challenging settings . they use a pipeline that first identifies mentions, then creates clusters within individual contexts . |
| Outcome: | The proposed model achieves state-of-the-art results on cross-document benchmarks and strong performance on long-document data while retaining top-tier results on traditional datasets. |
Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends (2024.acl-long)
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| Challenge: | Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks. |
| Approach: | They propose a pipeline that trains a state-of-the-art Coreference Resolution system within the constraints of an academic budget and trains with up to 0.006x the memory resources. |
| Outcome: | The proposed framework outperforms encoder-based discriminative systems on the CoNLL-2012 benchmark, training with up to 0.006x the memory resources and obtaining 170x faster inference compared to previous state-of-the-art systems. |
Interpretable Coreference Resolution Evaluation Using Explicit Semantics (2026.acl-long)
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| Challenge: | Existing evaluation methods for coreference resolution are limited by semantic and contextual information. |
| Approach: | They propose a semantically-enhanced evaluation framework for coreference resolution that assigns semantic labels to nominal mentions and propagates them to entire coreference clusters. |
| Outcome: | The proposed framework uncovers systematic weaknesses obscured by standard metrics. |