ARPA: Armenian Paraphrase Detection Corpus and Models

TL;DR

This paper introduces a new Armenian paraphrase detection corpus created through back translation, and develops BERT-based models that achieve state-of-the-art results in Armenian paraphrase detection.

Contribution

It presents the first semi-automatic method for Armenian paraphrase corpus creation and trains BERT models that perform competitively.

Findings

Created a 2360 sentence paraphrase dataset for Armenian.

BERT-based models achieved state-of-the-art performance.

Demonstrated effectiveness of back translation for low-resource language data augmentation.

Abstract

In this work, we employ a semi-automatic method based on back translation to generate a sentential paraphrase corpus for the Armenian language. The initial collection of sentences is translated from Armenian to English and back twice, resulting in pairs of lexically distant but semantically similar sentences. The generated paraphrases are then manually reviewed and annotated. Using the method train and test datasets are created, containing 2360 paraphrases in total. In addition, the datasets are used to train and evaluate BERTbased models for detecting paraphrase in Armenian, achieving results comparable to the state-of-the-art of other languages.