BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset
This addresses the problem of limited NLP resources for Bangla language users, though it appears incremental as it builds on existing paraphrase dataset methods for a new language.
The authors tackled the low-resource status of Bangla in NLP by creating BanglaParaphrase, a high-quality synthetic paraphrase dataset using a novel filtering pipeline, and demonstrated its viability through comparative analysis with existing works.
In this work, we present BanglaParaphrase, a high-quality synthetic Bangla Paraphrase dataset curated by a novel filtering pipeline. We aim to take a step towards alleviating the low resource status of the Bangla language in the NLP domain through the introduction of BanglaParaphrase, which ensures quality by preserving both semantics and diversity, making it particularly useful to enhance other Bangla datasets. We show a detailed comparative analysis between our dataset and models trained on it with other existing works to establish the viability of our synthetic paraphrase data generation pipeline. We are making the dataset and models publicly available at https://github.com/csebuetnlp/banglaparaphrase to further the state of Bangla NLP.