Accelerating Text Communication via Abbreviated Sentence Input
Accelerating Text Communication via Abbreviated Sentence Input
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DOI：
10.18653/v1/2021.acl-long.514
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Jiban Adhikary;Jamie Berger;K. Vertanen
中科院分区：
文献类型：
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作者：
Jiban Adhikary;Jamie Berger;K. Vertanen
键入文本消息中的每个字符可能需要比严格必要更多的时间或精力。跳过空格或其他字符可以能够加速输入并减少用户的物理输入努力。这对有运动障碍的人来说尤其重要。在一项大型的众包研究中，我们发现工作人员经常通过省略中间元音来缩写文本。我们设计了一个识别器优化扩展嘈杂的缩写输入，用户经常省略空格和中间词元音。我们表明，使用神经语言模型来选择对话式的训练文本和重新评分识别器的n个最好的句子提高了准确性。在从数百名用户收集的嘈杂的触摸屏数据中，我们发现即使省略了三分之一的字符，也可以准确地输入缩写。最后，在一项研究中，用户必须在每个键上停留一秒钟，句子缩写输入与具有单词预测的传统键盘具有竞争力。在练习之后，用户以每分钟9.6个单词的速度写缩写句子，而单词输入的速度为每分钟9.9个单词。
Typing every character in a text message may require more time or effort than strictly necessary. Skipping spaces or other characters may be able to speed input and reduce a user’s physical input effort. This can be particularly important for people with motor impairments. In a large crowdsourced study, we found workers frequently abbreviated text by omitting mid-word vowels. We designed a recognizer optimized for expanding noisy abbreviated input where users often omit spaces and mid-word vowels. We show using neural language models for selecting conversational-style training text and for rescoring the recognizer’s n-best sentences improved accuracy. On noisy touchscreen data collected from hundreds of users, we found accurate abbreviated input was possible even if a third of characters was omitted. Finally, in a study where users had to dwell for a second on each key, sentence abbreviated input was competitive with a conventional keyboard with word predictions. After practice, users wrote abbreviated sentences at 9.6 words-per-minute versus word input at 9.9 words-per-minute.