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Coreference resolution models reevaluated: Encoder models outperform, oldest generalizes best

By PulseAugur Editorial · [1 sources] ·

A new study reevaluates coreference resolution models, finding that encoder-based models outperform decoder-based ones in both accuracy and inference speed when controlling for language model size. The research also revealed that newer encoder-based models are not consistently more accurate, with the oldest tested model showing the best generalization across different text genres. This controlled reevaluation suggests that previous performance gains in coreference resolution may have been overestimated due to variations in experimental setups and language models. AI

IMPACT This research clarifies the performance of different coreference resolution model architectures, potentially guiding future development and selection for NLP tasks.

RANK_REASON The cluster contains an academic paper detailing a controlled reevaluation of coreference resolution models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Coreference resolution models reevaluated: Encoder models outperform, oldest generalizes best

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The cluster contains an academic paper detailing a controlled reevaluation of coreference resolution models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ian Porada, Xiyuan Zou, Jackie Chi Kit Cheung ·

    A Controlled Reevaluation of Coreference Resolution Models

    arXiv:2404.00727v3 Announce Type: replace Abstract: All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR model over another is due to the choice of language model or other factors, such …

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