论文标题
Banglanlg和Banglat5：评估孟加拉低资源自然语言生成的基准和资源
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla
论文作者
论文摘要
这项工作介绍了Banglanlg，这是一种评估孟加拉国自然语言生成（NLG）模型的综合基准，该模型是一种广泛使用但低资源的语言。我们在Banglanlg基准下汇总了六项具有挑战性的有条件文本生成任务，并在此过程中引入了有关对话生成的新数据集。此外，使用27.5 GB的孟加拉数据的干净语料库，我们预先列出了Banglat5，这是Bangla的序列到序列变压器语言模型。 Banglat5在所有这些任务中都实现了最先进的性能，表现优于多种多语言模型的绝对增益高达9％和32％的相对增益。我们正在https://github.com/csebuetnlp/banglanlg公开提供新的对话数据集和Banglat5模型，以期推进对Bangla NLG的未来研究。
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.
