# Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit arXiv: https://arxiv.org/abs/2308.08807 ยท HTML: https://mlsanity.com/paper/2308.08807/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 44 of its 595 references that are in the map. - First posted on arXiv 17 Aug 2023. ## Builds on (most cited first, 30 of 44) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018) - [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016) - [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015) - [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016) - [Semi-supervised Sequence Learning](https://mlsanity.com/paper/1511.01432/) (2015) - [Exploring the Limits of Language Modeling](https://mlsanity.com/paper/1602.02410/) (2016) - [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017) - [How2: A Large-scale Dataset for Multimodal Language Understanding](https://mlsanity.com/paper/1811.00347/) (2018) - [Sequence-to-Sequence Models Can Directly Translate Foreign Speech](https://mlsanity.com/paper/1703.08581/) (2017) - [Direct speech-to-speech translation with a sequence-to-sequence model](https://mlsanity.com/paper/1904.06037/) (2019) - [Yara Parser: A Fast and Accurate Dependency Parser](https://mlsanity.com/paper/1503.06733/) (2015) - [TENER: Adapting Transformer Encoder for Named Entity Recognition](https://mlsanity.com/paper/1911.04474/) (2019) - [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://mlsanity.com/paper/2006.00206/) (2020) - [Towards Universal Paraphrastic Sentence Embeddings](https://mlsanity.com/paper/1511.08198/) (2015) - [Universal Language Model Fine-tuning for Text Classification](https://mlsanity.com/paper/1801.06146/) (2018) - [Are All Good Word Vector Spaces Isomorphic?](https://mlsanity.com/paper/2004.04070/) (2020) - [From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers](https://mlsanity.com/paper/2005.00633/) (2020) - [Classifying Relations by Ranking with Convolutional Neural Networks](https://mlsanity.com/paper/1504.06580/) (2015) - [A New Dataset for Natural Language Inference from Code-mixed Conversations](https://mlsanity.com/paper/2004.05051/) (2020) - [A Survey of Word Embeddings Evaluation Methods](https://mlsanity.com/paper/1801.09536/) (2018) - [BSTC: A Large-Scale Chinese-English Speech Translation Dataset](https://mlsanity.com/paper/2104.03575/) (2021) - [Charagram: Embedding Words and Sentences via Character n-grams](https://mlsanity.com/paper/1607.02789/) (2016) - [Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training](https://mlsanity.com/paper/1810.11895/) (2018) - [TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer](https://mlsanity.com/paper/2210.11753/) (2022) - [Very Deep Convolutional Networks for Text Classification](https://mlsanity.com/paper/1606.01781/) (2016) - [Citation concept analysis (CCA) - A new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by two exemplary case studies including classic books by Thomas S. Kuhn and Karl R. Popper](https://mlsanity.com/paper/1905.12410/) (2019) - [A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages](https://mlsanity.com/paper/2102.06551/) (2021) - [Learning Soft Linear Constraints with Application to Citation Field Extraction](https://mlsanity.com/paper/1403.1349/) (2014) ## Built on by (most cited first, 1 of 1) - [Pragya: An AI-Based Semantic Recommendation System for Sanskrit Subhasitas](https://mlsanity.com/paper/2601.06607/) (2026) ## Abstract (arXiv) The primary focus of this thesis is to make Sanskrit manuscripts more accessible to the end-users through natural language technologies. The morphological richness, compounding, free word orderliness, and low-resource nature of Sanskrit pose significant challenges for developing deep learning solutions. We identify four fundamental tasks, which are crucial for developing a robust NLP technology for Sanskrit: word segmentation, dependency parsing, compound type identification, and poetry analysis. The first task, Sanskrit Word Segmentation (SWS), is a fundamental text processing task for any other downstream applications. However, it is challenging due to the sandhi phenomenon that modifies characters at word boundaries. Similarly, the existing dependency parsing approaches struggle with morphologically rich and low-resource languages like Sanskrit. Compound type identification is also challenging for Sanskrit due to the context-sensitive semantic relation between components. All these challenges result in sub-optimal performance in NLP applications like question answering and machine translation. Finally, Sanskrit poetry has not been extensively studied in computational linguistics. While addressing these challenges, this thesis makes various contributions: (1) The thesis proposes linguistically-informed neural architectures for these tasks. (2) We showcase the interpretability and multilingual extension of the proposed systems. (3) Our proposed systems report state-of-the-art performance. (4) Finally, we present a neural toolkit named SanskritShala, a web-based application that provides real-time analysis of input for various NLP tasks. Overall, this thesis contributes to making Sanskrit manuscripts more accessible by developing robust NLP technology and releasing various resources, datasets, and web-based toolkit.