We present a novel semantic framework for modeling linguistic expressions of generalization-generic, habitual, and episodic statements-as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information-including handengineered features and static (GloVe) and contextual (ELMo) word embeddings-for predicting expressions of generalization.

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Humans use language to accomplish a wide variety of tasks -asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increase the efficiency of adviceseeking online, as well as advice-giving in natural language generation systems.We present a dataset in English from two Reddit advice forums -r/AskParents and r/needadvice -annotated for whether sentences in posts contain advice or not. Our analysis reveals rich linguistic phenomena in advice discourse. We present preliminary models showing that while pre-trained language models are able to capture advice better than rulebased systems, advice identification is challenging, and we identify directions for future research.

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