Authorship Attribution is the task of creating an appropriate
characterization of text that captures the authors' writing style to identify
the original author of a given piece of text. With increased anonymity on the
internet, this task has become increasingly crucial in various security and
plagiarism detection fields. Despite significant advancements in other
languages such as English, Spanish, and Chinese, Bangla lacks comprehensive
research in this field due to its complex linguistic feature and sentence
structure. Moreover, existing systems are not scalable when the number of
author increases, and the performance drops for small number of samples per
author. In this paper, we propose the use of Average-Stochastic Gradient
Descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) architecture and an
effective transfer learning approach that addresses the problem of complex
linguistic features extraction and scalability for authorship attribution in
Bangla Literature (AABL). We analyze the effect of different tokenization, such
as word, sub-word, and character level tokenization, and demonstrate the
effectiveness of these tokenizations in the proposed model. Moreover, we
introduce the publicly available Bangla Authorship Attribution Dataset of 16
authors (BAAD16) containing 17,966 sample texts and 13.4+ million words to
solve the standard dataset scarcity problem and release six variations of
pre-trained language models for use in any Bangla NLP downstream task. For
evaluation, we used our developed BAAD16 dataset as well as other publicly
available datasets. Empirically, our proposed model outperformed
state-of-the-art models and achieved 99.8% accuracy in the BAAD16 dataset.
Furthermore, we showed that the proposed system scales much better even with an
increasing number of authors, and performance remains steady despite few
training samples.

提出了一种使用 AWD-LSTM 架构和有效的迁移学习方法来解决孟加拉文学领域的作者归属问题，通过分析不同的标记化方法，并介绍了一个包含 16966 份样本文本和 13.4 + 百万词汇的公开孟加拉作者归属数据集（BAAD16），此外还释放了六个预训练语言模型的变体。通过对 BAAD16 数据集及其他公开数据集的评估，实验证明所提出的模型优于目前的最先进模型，在 BAAD16 数据集上达到 99.8% 的准确率，而且在作者数量增加时，系统的可伸缩性表现更好，性能依然稳定。