Papers by Jivnesh Sandhan
Mahānāma: A Unique Testbed for Literary Entity Discovery and Linking (2025.emnlp-main)
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| Challenge: | High lexical variation, ambiguous references, and long-range dependencies make entity resolution in literary texts particularly challenging. |
| Approach: | They present a large-scale dataset for end-to-end Entity Discovery and Linking (EDL) in Sanskrit. |
| Outcome: | The proposed dataset is aligned with an English knowledge base to support cross-lingual linking. |
TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer (2022.findings-emnlp)
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| Challenge: | Existing approaches to SWS fail when encountering out-of-vocabulary tokens . lexicon driven approaches fail when dealing with out- of-vocal tokens, authors say . |
| Approach: | They propose a novel lexicon-driven shallow parser that takes into account sandhi phenomenon . they propose linguistically informed Sanskrit tokenizer module that encodes character input . |
| Outcome: | The proposed system outperforms the current state-of-the-art system by 7.2 points absolute gain on the benchmark datasets. |
SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes (2023.acl-demo)
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| Challenge: | SanskritShala is a neural-based Sanskrit NLP toolkit that is available as a web-based application . |
| Approach: | They propose a neural Sanskrit NLP toolkit that facilitates linguistic analyses for word segmentation, morphological tagging, dependency parsing, and compound type identification. |
| Outcome: | The proposed toolkit reports state-of-the-art performance on benchmark datasets . it is built with easy-to-use interactive data annotation features . |
CAPE: Context-Aware Personality Evaluation Framework for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing studies use a context-free approach to assess humans . existing studies use the Disney World test, which ignores real-world applications . |
| Approach: | They propose a framework to assess personality traits in large language models . they use conversational history to quantify the consistency of LLM responses . |
| Outcome: | The proposed framework improves consistency of responses in large language models . it also shows that conversational history enhances consistency and personality shifts . |
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing (2023.eacl-main)
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| Challenge: | Several strategies have been proposed to enhance performance in low-resource scenarios. |
| Approach: | They propose to use 5 low-resource strategies for dependency parsing for multiple languages . they use ensembled approach on 7 UD low-rsource languages based on their results . |
| Outcome: | The proposed approach improves on a low-resource language Sanskrit. |
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages (2021.eacl-srw)
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| Challenge: | Neural dependency parsing has been a success for many domains and languages, but the bottleneck of massive labelled data limits its effectiveness for low resource languages. |
| Approach: | They propose to use morphological knowledge to improve dependency parsing for morphology rich languages in a low-resource setting to perform experiments. |
| Outcome: | The proposed method achieves an average gain of 2 points (UAS) and 3.6 points (LAS) on 10 MRLs in low-resource settings. |
CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages (2024.emnlp-main)
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| Challenge: | Neural dependency parsing has achieved remarkable performance for low resource morphologically rich languages. |
| Approach: | They propose a self-supervised learning method to make the model robust to word order variations. |
| Outcome: | The proposed model shows a substantial gain of 3.03/2.95 points in 7 relatively free word order languages when compared to the best performing baseline. |
A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit (2022.coling-1)
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Jivnesh Sandhan, Ashish Gupta, Hrishikesh Terdalkar, Tushar Sandhan, Suvendu Samanta, Laxmidhar Behera, Pawan Goyal
| Challenge: | Previously, compounding is a problem of identifying semantic relations between components of a word. |
| Approach: | They propose a multi-task learning architecture which incorporates contextual information and enriches syntactic information using morphological tagging and dependency parsing as auxiliary tasks. |
| Outcome: | The proposed architecture shows 6.1 points accuracy and 7.7 points (F1-score) absolute gain in English and Marathi languages. |
Persona Jailbreaking in Large Language Models (2026.findings-eacl)
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| Challenge: | Existing studies focus on narrative or role-playing tasks and overlook how adversarial conversational history alone can reshape induced personas. |
| Approach: | They propose a framework that embeds semantically loaded cues into user queries to gradually induce reverse personas. |
| Outcome: | The proposed framework predictably shifts personas, triggers collateral changes in correlated traits, and exhibits stronger effects in multi-turn settings. |
DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit (2023.findings-emnlp)
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Jivnesh Sandhan, Yaswanth Narsupalli, Sreevatsa Muppirala, Sriram Krishnan, Pavankumar Satuluri, Amba Kulkarni, Pawan Goyal
| Challenge: | Multi-component compounding is a prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound is crucial for deciphering its meaning. |
| Approach: | They propose a task to identify nested spans of a multi-component compound and decode the implicit semantic relations between them. |
| Outcome: | The proposed framework surpasses the best baseline framework with an average improvement of 13.1 points in terms of Labeled Span Score and 5-fold enhancement in inference efficiency. |