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  1. There is an ongoing debate regarding how imperatives convey speaker endorsement. One line of approach builds it into the imperative meaning. Another posits weaker meanings. Indifference uses, like 'Go right! Go left! I don't care!', pose a challenge to the endorsement account. We reconcile the endorsement approach with such uses and argue that they can reduce to the speaker endorsing disjunctive prejacents, which results from one imperative operator taking a list of prejacents under its scope. This analysis predicts that intonational patterns that signal lists will facilitate disjunctive interpretations. We test and confirm this prediction in an experimental study. 
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  2. Contextual influences on language often exhibit substantial cross-lingual regularities; for example, we are more verbose in situations that require finer distinctions. However, these regularities are sometimes obscured by semantic and syntactic differences. Using a newly-collected dataset of color reference games in Mandarin Chinese (which we release to the public), we confirm that a variety of constructions display the same sensitivity to contextual difficulty in Chinese and English. We then show that a neural speaker agent trained on bilingual data with a simple multitask learning approach displays more human-like patterns of context dependence and is more pragmatically informative than its monolingual Chinese counterpart. Moreover, this is not at the expense of language-specific semantic understanding: the resulting speaker model learns the different basic color term systems of English and Chinese (with noteworthy cross-lingual influences), and it can identify synonyms between the two languages using vector analogy operations on its output layer, despite having no exposure to parallel data. 
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