• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Iurii Mokrii
Cycle Generative Adversarial Networks for seq2seq
8
2018
Sequence-to-sequence transduction is a wide class of problems. The one of the most famous such problem is neural machine translation. The common goal of problems in this class is to build the mapping between source and target sequence spaces, where the matched objects are consistent in some way. Supervised deep learning techniques, trained on the large amount of paired data, show the state-of-the-art results on the most of problems of this class. But in many cases, it's not possible to obtain a lot of pairs of sequences. On the other side, usually it is significantly cheaper to acquire unpaired sequences. In this paper in order to reduce the number of paired sequences I attempt to build semi-supervised sequence-to-sequence learning method by applying the ideas of cycle generative adversarial networks which are very successful on the image-to-image translation problem.

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