RI: Small: Bayesian Thinking on Your Feet---Embedding Generative Models in Reinforcement Learning for Sequentially Revealed Data

RI：小：贝叶斯思维在你的脚上——将生成模型嵌入到连续显示数据的强化学习中

• 批准号: 1320538
• 负责人: Jordan Boyd-Graber
• 金额: $ 50万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320538&HistoricalAwards=false
• 关键词: RI; Small; Bayesian; Thinking; Your

Machine learning algorithms cannot "think on their feet". When applied in practice, most approaches developed using traditional machine learning techniques wait for an entire input to arrive before they are able to provide an answer or react. While sufficient for some tasks, this is inappropriate for a large class of problems that require more immediate or incremental responses. This project develops new algorithms to address machine learning problems that require an algorithm to "think on its feet". These algorithms combine guesses about what input is likely appear in the future with actions that the algorithm should take now to provide useful, effective output in a timely fashion.One application of these new methods is simultaneous translation. This is the problem of taking problem of "observing" a sentence one word at a time in a foreign language, such as German, and providing a real-time running translation in a target language (like English). This is particularly difficult for language pairs that have significant syntactic divergences, such as object-verb order differences between foreign languages like German or Japanese (verb final) and target languages like English (verb medial). Like human simultaneous translators, machine learning algorithms must learn to predict the words that will appear at the end of a sentence. The project facilitates this prediction using a framework that combined word prediction and machine translation system.The project also uses the newly developed algorithms in academic settings to provide significant outreach to high school students and undergraduates, particularly in underrepresented communities.

机器学习算法不能“用脚思考”。 在实际应用中，大多数使用传统机器学习技术开发的方法都要等待整个输入到达，然后才能提供答案或做出反应。 虽然这对于某些任务来说已经足够了，但对于需要更立即或增量响应的一大类问题来说，这是不合适的。 该项目开发了新的算法来解决机器学习问题，这些问题需要一种算法来“思考”。 这些算法结合了联合收割机的猜测什么输入可能会出现在未来的行动，该算法现在应该采取及时提供有用的，有效的输出。这些新方法的应用之一是同步翻译。 这是一个在外语（如德语）中一次一个单词地“观察”句子的问题，并提供目标语言（如英语）的实时运行翻译。 这对于具有显著句法差异的语言对来说尤其困难，例如德语或日语（动词词尾）等外语与英语（动词中间）等目标语言之间的宾语-动词顺序差异。 就像人类同声传译一样，机器学习算法必须学会预测将出现在句末的单词。 该项目使用一个结合单词预测和机器翻译系统的框架来促进这一预测。该项目还使用新开发的算法在学术环境中为高中生和本科生提供重要的外展服务，特别是在代表性不足的社区。