Vishnu Dutt Sharma

Also published as: Vishnu Sharma


2019

pdf bib
Poetry to Prose Conversion in Sanskrit as a Linearisation Task: A Case for Low-Resource Languages
Amrith Krishna | Vishnu Sharma | Bishal Santra | Aishik Chakraborty | Pavankumar Satuluri | Pawan Goyal
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The word ordering in a Sanskrit verse is often not aligned with its corresponding prose order. Conversion of the verse to its corresponding prose helps in better comprehension of the construction. Owing to the resource constraints, we formulate this task as a word ordering (linearisation) task. In doing so, we completely ignore the word arrangement at the verse side. kāvya guru, the approach we propose, essentially consists of a pipeline of two pretraining steps followed by a seq2seq model. The first pretraining step learns task-specific token embeddings from pretrained embeddings. In the next step, we generate multiple possible hypotheses for possible word arrangements of the input %using another pretraining step. We then use them as inputs to a neural seq2seq model for the final prediction. We empirically show that the hypotheses generated by our pretraining step result in predictions that consistently outperform predictions based on the original order in the verse. Overall, kāvya guru outperforms current state of the art models in linearisation for the poetry to prose conversion task in Sanskrit.

2018

pdf bib
Building a Word Segmenter for Sanskrit Overnight
Vikas Reddy | Amrith Krishna | Vishnu Sharma | Prateek Gupta | Vineeth M R | Pawan Goyal
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit
Amrith Krishna | Bishal Santra | Sasi Prasanth Bandaru | Gaurav Sahu | Vishnu Dutt Sharma | Pavankumar Satuluri | Pawan Goyal
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The configurational information in sentences of a free word order language such as Sanskrit is of limited use. Thus, the context of the entire sentence will be desirable even for basic processing tasks such as word segmentation. We propose a structured prediction framework that jointly solves the word segmentation and morphological tagging tasks in Sanskrit. We build an energy based model where we adopt approaches generally employed in graph based parsing techniques (McDonald et al., 2005a; Carreras, 2007). Our model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one tenth of the task-specific training data. We find that the use of a graph based approach instead of a traditional lattice-based sequential labelling approach leads to a percentage gain of 12.6% in F-Score for the segmentation task.