Web searches often originate from conversations in which people engage before they perform a search. Therefore, conversations can be a valuable source of context with which to support the search process. We investigate whether spoken input from conversations can be used as a context to improve query auto-completion. We model the temporal dynamics of the spoken conversational context preceding queries and use these models to re-rank the query auto-completion suggestions. Data were collected from a controlled experiment and comprised conversations among 12 participant pairs conversing about movies or traveling. Search query logs during the conversations were recorded and temporally associated with the conversations. We compared the effects of spoken conversational input in four conditions: a control condition without contextualization; an experimental condition with the model using search query logs; an experimental condition with the model using spoken conversational input; and an experimental condition with the model using both search query logs and spoken conversational input. We show the advantage of combining the spoken conversational context with the Web-search context for improved retrieval performance. Our results suggest that spoken conversations provide a rich context for supporting information searches beyond current user-modeling approaches.

中文翻译：

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口语会话上下文改进了 Web 搜索中的查询自动完成

Web 搜索通常源自人们在执行搜索之前进行的对话。因此，对话可以成为支持搜索过程的有价值的上下文来源。我们调查对话中的语音输入是否可以用作提高查询自动完成的上下文。我们对查询前的口语对话上下文的时间动态进行建模，并使用这些模型对查询自动完成建议进行重新排序。数据是从对照实验中收集的，包括 12 对参与者之间关于电影或旅行的对话。对话期间的搜索查询日志被记录下来并与对话暂时关联。我们在四种情况下比较了口语对话输入的效果：没有语境化的控制条件；使用搜索查询日志的模型的实验条件；使用口语会话输入的模型的实验条件；以及使用搜索查询日志和口语对话输入的模型的实验条件。我们展示了将口语会话上下文与 Web 搜索上下文相结合以提高检索性能的优势。我们的结果表明，除了当前的用户建模方法之外，口语对话为支持信息搜索提供了丰富的上下文。我们展示了将口语会话上下文与 Web 搜索上下文相结合以提高检索性能的优势。我们的结果表明，除了当前的用户建模方法之外，口语对话为支持信息搜索提供了丰富的上下文。我们展示了将口语会话上下文与 Web 搜索上下文相结合以提高检索性能的优势。我们的结果表明，除了当前的用户建模方法之外，口语对话为支持信息搜索提供了丰富的上下文。