Generating subtitles with controllable length using natural language processing
Typ
Examensarbete för masterexamen
Program
Computer systems and networks (MPCSN), MSc
Publicerad
2022
Författare
Svensson, Joakim
Troksch, Victor
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Creating subtitles for video content is a task that has traditionally been performed
manually by subtitlers. When creating a subtitle, there are rules and guidelines for
how the text should be presented to the viewer. Therefore, a subtitle, translated
from one language to another, often contains linguistic compression in the form of
paraphrasing or removing parts of the dialogues. With advances in natural language
processing, subtitlers now have tools for machine translation and automated
speech recognition to assist them in their work. This thesis aims to explore various
methods for how to control the generated output length of a sequence-to-sequence
model, which are typically used for text generation and therefore also for machine
translation. We apply different modifications to both the model itself and the data
to control the output. Furthermore, this project makes use of transfer learning and
pre-trained models with the Transformer architecture. The length ratio method
produced the best results, in which it was possible to effectively control the output
length of a generated subtitle. We also discover that it was also possible to apply
this method for a translation model. Although it is a relatively simple method, it
produced the desired results with linguistic correctness.
Beskrivning
Ämne/nyckelord
Natural Language Processing , NLP , Transformer , seq2seq , text generation , BART , subtitles