The UN Parallel Corpus Annotated for Translation Direction
Elad Tolochinsky, Ohad Mosafi, Ella Rabinovich, Shuly Wintner

TL;DR
This paper presents a method to distinguish translated from original texts in the UN corpus with high accuracy, by modeling it as a classification problem using various feature extraction techniques, and provides a publicly available annotated corpus.
Contribution
It introduces a new annotated parallel corpus for translation direction and compares multiple classification methods for distinguishing translated and original texts.
Findings
Achieved up to 95% classification accuracy
Compared different feature extraction methods
Provided publicly available annotated corpus
Abstract
This work distinguishes between translated and original text in the UN protocol corpus. By modeling the problem as classification problem, we can achieve up to 95% classification accuracy. We begin by deriving a parallel corpus for different language-pairs annotated for translation direction, and then classify the data by using various feature extraction methods. We compare the different methods as well as the ability to distinguish between translated and original texts in the different languages. The annotated corpus is publicly available.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · Authorship Attribution and Profiling · Text and Document Classification Technologies
