AMR parse quality prediction [Source Code]
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https://heidata.uni-heidelberg.de/citation?persistentId=doi:10.11588/DATA/STHBGW
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资源简介:
<p>Accuracy prediction for AMR parsing predicts 33 accuracy metrics for a given sentence and its (automatic) AMR parse</p>
<p><strong>Abstract (Opitz and Frank, 2019):</strong></p>
<p>Semantic proto-role labeling (SPRL) is an alternative to semantic role labeling (SRL) that moves beyond a categorical definition of roles, following Dowty's feature-based view of proto-roles. This theory determines agenthood vs. patienthood based on a participant's instantiation of more or less typical agent vs. patient properties, such as, for example, volition in an event. To perform SPRL, we develop an ensemble of hierarchical models with self-attention and concurrently learned predicate-argument-markers. Our method is competitive with the state-of-the art, overall outperforming previous work in two formulations of the task (multi-label and multi-variate Likert scale prediction). In contrast to previous work, our results do not depend on gold argument heads derived from supplementary gold tree banks.</p>
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提供机构:
heiDATA
创建时间:
2019-07-12



