ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair

Alham Fikri Aji; Mahendra Data; Nadhifa Zulfa; Philip Arthur; Radityo Eko Prasojo; Salma Qonitah; Suci Fitriany; Tirana Noor Fatyanosa; Tomi Santoso

ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair

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arxiv 2205.04651 v1 pith:FOPO24RY submitted 2022-05-10 cs.CL

Alham Fikri Aji , Tirana Noor Fatyanosa , Radityo Eko Prasojo , Philip Arthur , Suci Fitriany , Salma Qonitah , Nadhifa Zulfa , Tomi Santoso

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Mahendra Data

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classification cs.CL

keywords diverseparaphrasesynthetictranslationaccordingcorpusgeneratelexically

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We release our synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Romanian, Russian, Swedish, Vietnamese, and Chinese. Our method relies only on monolingual data and a neural machine translation system to generate paraphrases, hence simple to apply. We generate multiple translation samples using beam search and choose the most lexically diverse pair according to their sentence BLEU. We compare our generated corpus with the \texttt{ParaBank2}. According to our evaluation, our synthetic paraphrase pairs are semantically similar and lexically diverse.

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MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models
cs.CL 2025-08 conditional novelty 6.0 of 10

A new human-corrected Marathi paraphrase detection corpus with 8,000 pairs in five difficulty buckets, benchmarked with BERT models, with MahaBERT reaching 88.7% F1.

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