Bibliographic Details
| Title: |
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla |
| Authors: |
Bhattacharjee, Abhik, Hasan, Tahmid, Ahmad, Wasi Uddin, Shahriyar, Rifat |
| Publication Year: |
2022 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computation and Language |
| More Details: |
This work presents BanglaNLG, a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language. We aggregate six challenging conditional text generation tasks under the BanglaNLG benchmark, introducing a new dataset on dialogue generation in the process. Furthermore, using a clean corpus of 27.5 GB of Bangla data, we pretrain BanglaT5, a sequence-to-sequence Transformer language model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming several multilingual models by up to 9% absolute gain and 32% relative gain. We are making the new dialogue dataset and the BanglaT5 model publicly available at https://github.com/csebuetnlp/BanglaNLG in the hope of advancing future research on Bangla NLG.
Comment: Findings of EACL 2023 (camera-ready) |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2205.11081 |
| Accession Number: |
edsarx.2205.11081 |
| Database: |
arXiv |
