Papers by Nicole Macher

2 papers
MiST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text (2022.findings-emnlp)

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Challenge: modal verbs are used for hedges, but they may also denote abilities and restrictions in scientific texts . modals are often used for hedging, but prior work on this topic has been limited .
Approach: They propose a dataset that contains 3737 modal instances in five scientific domains . they evaluate a set of competitive neural architectures to model the distinctions in MIST .
Outcome: The proposed dataset contains 3737 modal instances in five scientific domains . leveraging non-scientific data is of limited benefit for modeling the distinctions in MIST .
Do we read what we hear? Modeling orthographic influences on spoken word recognition (2021.eacl-srw)

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Challenge: Existing theories and models of spoken word recognition focus on accessing lexical knowledge given an acoustic realization of a word form.
Approach: They propose two models that instantiate hypotheses regarding the influence of orthography on spoken word recognition.
Outcome: The proposed models reproduce human-like behavior in different ways and provide testable hypotheses for future research on the source of orthographic effects in spoken word recognition.

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