Crossword puzzles have traditionally served not only as entertainment but
also as an educational tool that can be used to acquire vocabulary and language
proficiency. One strategy to enhance the educational purpose is
personalization, such as including more words on a particular topic. This paper
focuses on the case of encouraging people's interest in news and proposes a
framework for automatically generating news-centric crossword puzzles. We
designed possible scenarios and built a prototype as a constraint satisfaction
and optimization problem, that is, containing as many news-derived words as
possible. Our experiments reported the generation probabilities and time
required under several conditions. The results showed that news-centric
crossword puzzles can be generated even with few news-derived words. We
summarize the current issues and future research directions through a
qualitative evaluation of the prototype. This is the first proposal that a
formulation of a constraint satisfaction and optimization problem can be
beneficial as an educational application.

本研究提出了一个自动生成以新闻为中心的填字游戏的框架，通过包含尽可能多的新闻相关单词，来增加人们对新闻的兴趣，并通过对原型的定性评估总结了当前问题和未来研究方向。这是首个提出将约束满足和优化问题的形式化应用于教育领域的建议。

作为约束满足和优化问题的生成以新闻为中心的纵横填字谜

Generating News-Centric Crossword Puzzles As A Constraint Satisfaction  and Optimization Problem

We study the effectiveness of several techniques to personalize end-to-end
speech models and improve the recognition of proper names relevant to the user.
These techniques differ in the amounts of user effort required to provide
supervision, and are evaluated on how they impact speech recognition
performance. We propose using keyword-dependent precision and recall metrics to
measure vocabulary acquisition performance. We evaluate the algorithms on a
dataset that we designed to contain names of persons that are difficult to
recognize. Therefore, the baseline recall rate for proper names in this dataset
is very low: 2.4%. A data synthesis approach we developed brings it to 48.6%,
with no need for speech input from the user. With speech input, if the user
corrects only the names, the name recall rate improves to 64.4%. If the user
corrects all the recognition errors, we achieve the best recall of 73.5%. To
eliminate the need to upload user data and store personalized models on a
server, we focus on performing the entire personalization workflow on a mobile
device.

通过使用不同的个性化技术来提高语音识别的效果，我们提出了一种基于关键词精度和召回率的词汇获取性能评估方法，并在设计的包含难以识别人名的数据集上进行了算法评估，其中，数据合成方法可以将基线的人名召回率从 2.4% 提高到 48.6%。在进行名字修正的情况下，名字召回率可提高至 64.4%。通过在移动设备上执行整个个性化工作流程，我们摆脱了上传用户数据和在服务器上存储个性化模型的需要。