Sanskrit Sandhi Splitting using seq2(seq)^2

• Main Authors: Aralikatte, Rahul, Gantayat, Neelamadhav, Panwar, Naveen, Sankaran, Anush, Mani, Senthil
• Format: Journal Article
• Language: English
• Published: 01-01-2018
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.1801.00428

In Sanskrit, small words (morphemes) are combined to form compound words
through a process known as Sandhi. Sandhi splitting is the process of splitting
a given compound word into its constituent morphemes. Although rules governing
word splitting exists in the language, it is highly challenging to identify the
location of the splits in a compound word. Though existing Sandhi splitting
systems incorporate these pre-defined splitting rules, they have a low accuracy
as the same compound word might be broken down in multiple ways to provide
syntactically correct splits.
In this research, we propose a novel deep learning architecture called Double
Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95%
accuracy, and (ii) predicts the constituent words (learning the Sandhi
splitting rules) with 79.5% accuracy, outperforming the state-of-art by 20%.
Additionally, we show the generalization capability of our deep learning model,
by showing competitive results in the problem of Chinese word segmentation, as
well.