A Controlled Reevaluation of Coreference Resolution Models
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
arXiv:2606. 05444v1 Announce Type: cross Abstract: Coreference resolution is a core NLP task, having a broad range of downstream applications, e.
arXiv:2606. 17950v1 Announce Type: cross Abstract: Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains.
The paper investigates whether adding Abstract Meaning Representation (AMR) data to large language models (LLMs) improves performance on downstream tasks. By reproducing recent studies and applying a consistent hyperparameter protocol, the authors find that text-only baselines match or surpass AMR-augmented models. A perplexity-based probe shows that AMR does not provide LLMs with additional relational knowledge, suggesting no clear benefit from AMR augmentation.
arXiv:2606. 24841v1 Announce Type: new Abstract: Prompt-based learning has emerged as a dominant paradigm in natural language processing.
arXiv:2601.06347v3 Announce Type: replace Abstract: Recent progress in universal multilingual named entity recognition (NER) has been driven by multilingual transformer models, task-specific architec...
arXiv:2608.30609v1 Announce Type: cross Abstract: Large language models are increasingly capable in general, but their utility can remain modest in niche or understudied areas. One approach to addres...