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CAREER: Drawing inferences for human-like language understanding

职业：为类人语言理解做出推论

基本信息

批准号：
1845122
负责人：
Marie-Catherine de Marneffe
金额：
$ 49.99万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845122&HistoricalAwards=false
关键词：
CAREER Drawing inferences human like

项目摘要

When dealing with language, readers and listeners understand more than just the literal meaning of the words they read or hear; they also draw inferences from them. For instance, if someone tells you "I said you were mad to come over at this time. It's a world event. Do you know that Venice is packed with visitors?", they will likely infer that Venice is indeed packed with visitors. However in "How long has she been like this? Did you see a doctor? Do you know that it is incurable?", they will not infer that it is incurable, even though both events are in a question and embedded under the same string of words "do you know". Different factors, like the tone used or world knowledge, play a role in deriving these inferences. The project aims at studying these factors and developing broad-coverage models that automatically capture inferences. Such models have implications for natural language processing (NLP) tasks that require an accurate inference process, such as information extraction. Further, to achieve human-like language understanding, it is not only crucial for NLP technologies to develop models that capture what is conveyed in language without being explicitly said, but to also assess whether the inferences are systematic for most people, or whether different interpretations arise. This project investigates how the variability present in "common sense" data that come from people's intuitions can be accurately represented in the type of datasets on which NLP systems are currently built, and thereby be captured. Recently a large body of work in NLP has focused on deep learning, hill-climbing on new tasks and benchmarks. However such ventures do not help with the understanding of the details of human language or in determining which features actually matter for language processing. This project targets both categorical and non-categorical inferences in a diverse set: inferences about sentiment, agreement and speaker commitment (whether speakers are committed to the truth of the events they describe), and redefining the kind of benchmarks needed to achieve human-like natural language understanding. It investigates how a better synergy between data-driven methods and the use of specialized linguistic features can lead to fundamental advances in NLP systems. The project also quantitatively studies the interactions of linguistic features on a large amount of naturally occurring examples, and has thus an impact not only for NLP but also for linguistic theories. Results will include a better grasp of how linguistic insights can be used to automatically achieve human-level understanding; a publicly available data that better fit human intuitions on language than current datasets do and can thereby serve to sharpen NLP models; and course materials for students and demos for the general public that raise awareness of societal problems engendered by social media, emphasize the importance of what gets conveyed by language beyond the explicit string of words in everyday communication, and demonstrate what can be achieved when research in linguistics and computer science is combined.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在处理语言时，读者和听众理解的不仅仅是他们读到或听到的单词的字面意思；他们也会从中得出推论。例如，如果有人告诉你“我说你在这个时候过来是疯了。这是一个世界性的事件。你知道威尼斯挤满了游客吗？”，他们很可能会推断出威尼斯确实挤满了游客。然而在“她这样多久了？”你看医生了吗？你知道这是不可治愈的吗？”，他们不会推断出这是不可治愈的，即使这两个事件都是在一个问题中，并且嵌入在同一串单词“你知道吗”之下。不同的因素，如使用的语气或世界知识，在得出这些推论时发挥了作用。该项目旨在研究这些因素并开发自动捕获推断的广泛覆盖模型。这些模型对需要精确推理过程的自然语言处理（NLP）任务（如信息提取）具有启示意义。此外，为了实现类似人类的语言理解，NLP技术不仅要开发能够捕捉语言中没有明确表达的内容的模型，而且还要评估对大多数人来说推理是否系统，或者是否会产生不同的解释。该项目研究了如何将来自人们直觉的“常识”数据中的可变性准确地表示为目前构建NLP系统的数据集类型，从而捕获这些数据集。最近，NLP领域的大量工作都集中在深度学习、新任务和基准的爬山上。然而，这样的尝试无助于理解人类语言的细节，也无助于确定哪些特征对语言处理真正重要。这个项目的目标是在不同的集合中进行分类和非分类推理：关于情绪、协议和说话者承诺的推理（说话者是否承诺他们所描述的事件的真实性），并重新定义实现类似人类自然语言理解所需的基准。它研究了数据驱动方法和专业语言特征的使用之间的更好协同如何导致NLP系统的根本进步。该项目还定量研究了大量自然发生的例子中语言特征的相互作用，因此不仅对NLP而且对语言学理论都有影响。结果将包括更好地掌握如何使用语言洞察力来自动实现人类水平的理解；一个公开可用的数据，比当前的数据集更符合人类对语言的直觉，从而可以用来提高NLP模型；面向学生的课程材料和面向公众的演示，提高了人们对社交媒体引发的社会问题的认识，强调了日常交流中明确的单词串之外语言所传达的内容的重要性，并展示了语言学和计算机科学研究相结合可以取得的成就。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。