Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker's intentions and the listener's perceptions in both cases.

People often share personal narratives in order to seek advice from others. To properly infer the narrator's intention, one needs to apply a certain degree of common sense and social intuition. To test the capabilities of NLP systems to recover such intuition, we introduce the new task of inferring what is the adviceseeking goal behind a personal narrative. We formulate this as a cloze test, where the goal is to identify which of two advice-seeking questions was removed from a given narrative.The main challenge in constructing this task is finding pairs of semantically plausible adviceseeking questions for given narratives. To address this challenge, we devise a method that exploits commonalities in experiences people share online to automatically extract pairs of questions that are appropriate candidates for the cloze task. This results in a dataset of over 20,000 personal narratives, each matched with a pair of related advice-seeking questions: one actually intended by the narrator, and the other one not. The dataset covers a very broad array of human experiences, from dating, to career options, to stolen iPads. We use human annotation to determine the degree to which the task relies on common sense and social intuition in addition to a semantic understanding of the narrative. By introducing several baselines for this new task we demonstrate its feasibility and identify avenues for better modeling the intention of the narrator.

Gender bias is an increasingly important issue in sports journalism. In this work, we propose a language-model-based approach to quantify differences in questions posed to female vs. male athletes, and apply it to tennis post-match interviews. We find that journalists ask male players questions that are generally more focused on the game when compared with the questions they ask their female counterparts. We also provide a fine-grained analysis of the extent to which the salience of this bias depends on various factors, such as question type, game outcome or player rank.

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Group discussions are a way for individuals to exchange ideas and arguments in order to reach better decisions than they could on their own. One of the premises of productive discussions is that better solutions will prevail, and that the idea selection process is mediated by the (relative) competence of the individuals involved. However, since people may not know their actual competence on a new task, their behavior is influenced by their self-estimated competence -that is, their confidence -which can be misaligned with their actual competence.Our goal in this work is to understand the effects of confidencecompetence misalignment on the dynamics and outcomes of discussions. To this end, we design a large-scale natural setting, in the form of an online team-based geography game, that allows us to disentangle confidence from competence and thus separate their effects.We find that in task-oriented discussions, the more-confident individuals have a larger impact on the group's decisions even when these individuals are at the same level of competence as their teammates. Furthermore, this unjustified role of confidence in the decisionmaking process often leads teams to under-perform. We explore this phenomenon by investigating the effects of confidence on conversational dynamics. For example, we take up the question: do more-confident people introduce more ideas than the less-confident, or do they introduce the same number of ideas but their ideas get more uptake? Moreover, we show that the language people use is more predictive of a person's confidence level than their actual competence. This also suggests potential practical applications, given that in many settings, true competence cannot be assessed before the task is completed, whereas the conversation can be tracked during the course of the problem-solving process.