This paper investigates the distribution of epistemic modals in attitude contexts in three Romance languages, as well as their potential interaction with mood selection. We show that epistemics can appear in complements of attitudes of acceptance (Stalnaker 1984), but not desideratives or directives; in addition, emotive doxastics (hope, fear) and dubitatives (doubt) permit epistemic possibility modals, but not their necessity counterparts. We argue that the embedding differences across attitudes indicate that epistemics are sensitive to the type of attitude an attitude predicate reports. We show that this sensitivity can be derived by adopting two types of proposals from the literature on epistemic modality and on attitude verbs: First, we assume that epistemics do not target knowledge uniformly, but rather quantify over an information state determined by the content of the embedding attitude (Hacquard 2006, Yalcin 2007. In turn, we adopt a fundamental split in the semantics of attitude verbs between 'representational ' and 'non-representational' attitudes (Bolinger 1968): representational attitudes quantify over an information state (e.g., a set of beliefs for believe), which, we argue, epistemic modals can be anaphoric to. Non-representational attitudes do not quantify over an information state; instead, they combine with their complement via a comparison with contextually-provided alternatives using a logic of preference (cf.

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More and more of the information available on the web is dialogic, and a significant portion of it takes place in online forum conversations about current social and political topics. We aim to develop tools to summarize what these conversations are about. What are the CENTRAL PROPOSITIONS associated with different stances on an issue; what are the abstract objects under discussion that are central to a speaker's argument? How can we recognize that two CENTRAL PROPOSITIONS realize the same FACET of the argument? We hypothesize that the CENTRAL PROPOSITIONS are exactly those arguments that people find most salient, and use human summarization as a probe for discovering them. We describe our corpus of human summaries of opinionated dialogs, then show how we can identify similar repeated arguments, and group them into FACETS across many discussions of a topic. We define a new task, ARGUMENT FACET SIMILARITY (AFS), and show that we can predict AFS with a .54 correlation score, versus an ngram system baseline of .39 and a semantic textual similarity system baseline of .45.

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Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.