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Shortening of the results of machine translation using paraphrasing dataset

Publication at Faculty of Mathematics and Physics |
2023

Abstract

As machine translation applications continue to expand into the realm of real-time events, the need for faster and more concise translation becomes increasingly important. One such application is simultaneous speech translation, an emission of subtitles in the target language given speech in the source language.

In this work, we focus on easing reader's comprehension of subtitles by making the translation shorter while preserving its informativeness. For this, we use the S, M and L version of the Paraphrase Database (PPDB), and exploit their property that some of the paraphrasing rules differ in length of the left and right side.

Selecting rules that make the output shorter, we fine-tune an MT model to naturally generate shorter translations. The results show that the model's conciseness improves by up to 0.61%, which leaves the space for improvements using bigger versions of PPDB in future work.