Understanding priors in language generation models

我的研究目标是通过理解语言的底层语法和单词之间的语义关系，使算法生成文本成为可能。重点是探索不同的神经网络架构。其中一个研究案例是机器翻译中的幻觉。在机器翻译中，幻觉是“语义上完全不正确，但语法上也可行”的代。深入了解这一现象很重要，不仅因为幻觉会损害商业系统用户的信心。NMT中的幻觉是令人惊讶的定义明确的高度病态输出案例，任何关于它们如何或为什么发生的见解将有助于回答为什么变形金刚产生语言（对人类来说是连贯的）这个问题？

The objective of my research is what makes it possible for algorithms to generate text, both by understanding the underlying grammar of language and the semantic relationship between words. The focus is on the exploration of different neural network architectures.One case study is that of hallucinations in machine trainslation.In machine translation, hallucinations are generations that are "completely semantically incorrect and also grammatically viable". It is important to understand in depth this phenomenon, not only because hallucinations can harm the confidence of users of commercial systems. Hallucinations in NMT are surprisingly well-defined cases of highly pathological outputs and any insight on how or why they happen will contribute towards answering the question why do transformers generate language (that is coherent to humans)?