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Nafise Sadat Moosavi

ACL 2026×2可解释性评测与基准数据与预训练对齐与偏好多模态
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #6/6ACL 2026Poster· Natural Language Processing

Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning

Maggie Mi · Golzar Atefi · Atsuki Yamaguchi · Felix Gers · Aline Villavicencio · Nafise Sadat Moosavi

摘要

Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability is thought to predict syntactic flexibility. Usage-based accounts instead attribute idiom behaviour to distributional experience, such as speaker...

可解释性评测与基准数据与预训练OpenReview 项目页
作者位次 #4/5ACL 2026Findings· Findings of ACL

RIGOURATE : Quantifying Scientific Exaggeration with Evidence-Aligned Claim Evaluation

Joseph James · Chenghao Xiao · Yucheng Li · Nafise Sadat Moosavi · Chenghua Lin

摘要

Scientific rigour tends to be sidelined in favour of bold statements, leading authors to overstate claims beyond what their results support. We present RIGOURATE, a two-stage multimodal framework that retrieves supporting evidence from a paper’s body and assigns each claim an overstatement score....

对齐与偏好多模态强化学习与后训练OpenReview 项目页

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