Questions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange. The surface form of a question can signal the intention and background of the person asking it, as well as the nature of their relation with the interlocutor. While the informational nature of questions has been extensively examined in the context of question-answering applications, their rhetorical aspects have been largely understudied. In this work we introduce an unsupervised methodology for extracting surface motifs that recur in questions, and for grouping them according to their latent rhetorical role. By applying this framework to the setting of question sessions in the UK parliament, we show that the resulting typology encodes key aspects of the political discourse---such as the bifurcation in questioning behavior between government and opposition parties---and reveals new insights into the effects of a legislator's tenure and political career ambitions.
Pragmatic dimensions One important pragmatic dimension of questions that has been previously studied computationally is their level of politeness @cite_8 @cite_0 ; in the specific context of making requests, politeness was shown to correlate with the social status of the asker. studied another rhetorical aspect by examining linguistic attributes distinguishing serviceable requests addressed to police on social media from general conversation. Previous research has also been directed at identifying rhetorical questions @cite_26 and understanding the motivations of their askers'' @cite_7 . Using the relationship between questions and answers, our work examines the rhetorical and social aspect of questions without predefining a pragmatic dimension and without relying on labeled data. We also complement these efforts in analyzing a broader range of situations in which questions may be posed without an information-seeking intent.
@cite_0 @cite_26 @cite_7 @cite_8
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We present an interpretable neural network approach to predicting and understanding politeness in natural language requests. Our models are based on simple convolutional neural networks directly on raw text, avoiding any manual identification of complex sentiment or syntactic features, while performing better than such feature-based models from previous work. More importantly, we use the challenging task of politeness prediction as a testbed to next present a much-needed understanding of what these successful networks are actually learning. For this, we present several network visualizations based on activation clusters, first derivative saliency, and embedding space transformations, helping us automatically identify several subtle linguistics markers of politeness theories. Further, this analysis reveals multiple novel, high-scoring politeness strategies which, when added back as new features, reduce the accuracy gap between the original featurized system and the neural model, thus providing a clear quantitative interpretation of the success of these neural networks. Social media provides a platform for seeking information from a large user base. Information seeking in social media, however, occurs simultaneously with users expressing their viewpoints by making statements. Rhetorical questions have the form of a question but serve the function of a statement and might mislead platforms assisting information seeking in social media. It becomes difficult to identify rhetorical questions as they are not syntactically different from other questions. In this paper, we develop a framework to identify rhetorical questions by modeling the motivations of the users to post them. We focus on one motivation of the users drawing from linguistic theories, to implicitly convey a message. We develop a framework from this motivation to identify rhetorical questions in social media and evaluate the framework using questions posted on Twitter. This is the first framework to model the motivations for posting rhetorical questions to identify them on social We propose a computational framework for identifying linguistic aspects of politeness. Our starting point is a new corpus of requests annotated for politeness, which we use to evaluate aspects of politeness theory and to uncover new interactions between politeness markers and context. These findings guide our construction of a classifier with domain-independent lexical and syntactic features operationalizing key components of politeness theory, such as indirection, deference, impersonalization and modality. Our classifier achieves close to human performance and is effective across domains. We use our framework to study the relationship between politeness and social power, showing that polite Wikipedia editors are more likely to achieve high status through elections, but, once elevated, they become less polite. We see a similar negative correlation between politeness and power on Stack Exchange, where users at the top of the reputation scale are less polite than those at the bottom. Finally, we apply our classifier to a preliminary analysis of politeness variation by gender and community.