Conversational Search and Question Answering Sample Clauses

Conversational Search and Question Answering. The topic of chatbots and conversational answer seeking has recently become quite popular. ▇.▇▇▇▇▇▇▇▇▇ and ▇.▇▇▇▇▇▇▇▇ [153] defined a set of required properties and designed a theoretical model of interactions in a conversational search. ▇.▇▇▇▇▇ et al. [103] released a dataset for conversational question an- swering, which was built by a converting complex multi-part questions from WikiTables dataset to a sequence of related questions using crowdsourcing. Authors identified major challenges in this data as resolving references to previously mentioned entities and semantic matching. Much work has been done in the area of comparing user interactions with a human and a computer. There are varying opinions on the subject. ▇▇▇▇▇▇▇ et al. [64] found no significant differences in how Twitter users treated a social bot, whether it was perceived as a human or not. In turn, Cl´ement and Guitton [54] report that the way bots are perceived varies with the role they play. They found that “invasive” Wikipedia bots received more “polarizing” feedback – both positive and negative – compared to the bots that carried out “silent helper” functions. The similar result is reported by ▇▇▇▇▇▇ et al. [141] – Stackoverflow bot receives more negative feedback for false answers when its identity as an automatic program is revealed. Another work by Aharoni and ▇▇▇▇▇▇▇▇ [6] reports mixed results from participants who underwent a mock interview with a human and an automatic system. The authors report that there were no explicit differences in the interviewer perception described by the participants, although the authors noticed significant differences in people’s behavior – when talking to a human interviewer they made greater effort to speak, smiled more, and were more affected by a rejection. In a study by Luger and ▇▇▇▇▇▇ [132], 14 people were interviewed about their experience with an intelligent assistant that they use in their daily life. The authors report on people’s experiences, expectations, discuss scenarios of successes and failures of conversational agents. They report that the most frequent types of tasks are relatively simple – weather updates and checking reminders. Early QA studies considered users the sole proactive part asking refining questions and clarifying on system’s response [59]. QA with a more active system’s role was investigated within the complex interactive QA (ciQA) TREC track: assessors provided additional information in various forms to live QA systems...

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