Unsupervised Transcription to Orthography with Neural Networks

Student: Stepanov Artem

Supervisor: Francis M. Tyers

Educational Programme: Computational Linguistics (Master)

Year of Graduation: 2019

In this work we consider transcription to orthography task; that is, given a word’s IPA transcription, we predict its orthographic form. This task is a subtask of decipherment problem, where we consider IPA symbols as ciphertext and alphabet symbols as plaintext. We apply various neural networks architectures to solve this problem and run our experiments on data from European languages.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses