论文标题
能够将整理语言翻译成词干的语言吗？使用机器翻译进行证人本地化
Can the Language of the Collation be Translated into the Language of the Stemma? Using Machine Translation for Witness Localization
论文作者
论文摘要
Stemmatology是一个语言学的一个子领域，一种方法可以理解文本变体的复制历史（传统的见证人）是生成进化树。在系统发育学和干词学的姐妹学科之间部分共享计算方法。在2022年，自然通讯中的一项监视器发现，深度学习（DL）在许多领域都带来了重大改进（Krohn等，2020年）在系统发育学方面仅取得了较小的成功，并且“很难想象端到端DL DL模型直接从近乎未来的数据中估算系统发生的模型”（Sapoval et al Al a al and P. 20222222222222222222222222222222222222年）。在StemMatology中，迄今为止根本没有已知的DL方法。在本文中，我们提出了一种新的DL方法，用于将手稿放置在Stemma上并证明其潜力。这可以扩展到系统发育学，在该系统中，DNA的通用代码可能是该方法使用序列进行序列基于序列的神经网络以获取树距离的更好的先决条件。
Stemmatology is a subfield of philology where one approach to understand the copy-history of textual variants of a text (witnesses of a tradition) is to generate an evolutionary tree. Computational methods are partly shared between the sister discipline of phylogenetics and stemmatology. In 2022, a surveypaper in nature communications found that Deep Learning (DL), which otherwise has brought about major improvements in many fields (Krohn et al 2020) has had only minor successes in phylogenetics and that "it is difficult to conceive of an end-to-end DL model to directly estimate phylogenetic trees from raw data in the near future"(Sapoval et al. 2022, p.8). In stemmatology, there is to date no known DL approach at all. In this paper, we present a new DL approach to placement of manuscripts on a stemma and demonstrate its potential. This could be extended to phylogenetics where the universal code of DNA might be an even better prerequisite for the method using sequence to sequence based neural networks in order to retrieve tree distances.
