加强文本输入研究中的作文任务:引出困难文本,改进错误率计算

Dylan Gaines, P. Kristensson, K. Vertanen
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引用次数: 7

摘要

文本输入研究的参与者通常会复制短语或撰写新颖的信息。合成任务模拟实际的用户行为,可以让研究人员更好地理解系统在现实中的表现。作文的一个问题是,参与者可能倾向于写简单的文本,即只包含常见单词的文本。这样简单的文本不足以探讨控制文本输入方法的所有因素,例如它的纠错功能。我们通过两种方式增强了组合任务。首先,我们展示了参与者可以根据简单的指令调整他们作文的难度。虽然写复杂的信息要花更多的时间,但它们更长,有更多的困难单词,并且需要更多的纠错功能。其次,我们比较了获取参与者预期文本的两种方法,并将这两种方法与先前提出的众包评判程序进行了比较。我们发现参与者提供的参考文献更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing the Composition Task in Text Entry Studies: Eliciting Difficult Text and Improving Error Rate Calculation
Participants in text entry studies usually copy phrases or compose novel messages. A composition task mimics actual user behavior and can allow researchers to better understand how a system might perform in reality. A problem with composition is that participants may gravitate towards writing simple text, that is, text containing only common words. Such simple text is insufficient to explore all factors governing a text entry method, such as its error correction features. We contribute to enhancing composition tasks in two ways. First, we show participants can modulate the difficulty of their compositions based on simple instructions. While it took more time to compose difficult messages, they were longer, had more difficult words, and resulted in more use of error correction features. Second, we compare two methods for obtaining a participant’s intended text, comparing both methods with a previously proposed crowdsourced judging procedure. We found participant-supplied references were more accurate.
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