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Modal verbs, such as "can", "may", and "must", are commonly used in dailycommunication to convey the speaker's perspective related to the likelihoodand/or mode of the proposition. They can differ greatly in meaning depending onhow they're used and the context of a sentence (e.g. "They 'must' help eachother out." vs. "They 'must' have helped each other out.") Despite theirpractical importance in natural language understanding, linguists have yet toagree on a single, prominent framework for the categorization of modal verbsenses. This lack of agreement stems from high degrees of flexibility andpolysemy from the modal verbs, making it more difficult for researchers toincorporate insights from this family of words into their work. This workpresents Moverb dataset, which consists of 27,240 annotations of modal verbsenses over 4,540 utterances containing one or more sentences from socialconversations. Each utterance is annotated by three annotators using twodifferent theoretical frameworks (i.e., Quirk and Palmer) of modal verb senses.We observe that both frameworks have similar inter-annotator agreements,despite having different numbers of sense types (8 for Quirk and 3 for Palmer).With the RoBERTa-based classifiers fine-tuned on \dataset, we achieve F1 scoresof 82.2 and 78.3 on Quirk and Palmer, respectively, showing that modal verbsense disambiguation is not a trivial task. Our dataset will be publiclyavailable with our final version.

Quirk or Palmer: A Comparative Study of Modal Verb Frameworks with Annotated Datasets