Papers by Laxmidhar Behera
TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer (2022.findings-emnlp)
Copied to clipboard
| 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)
Copied to clipboard
| 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 . |
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing (2023.eacl-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit (2022.coling-1)
Copied to clipboard
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. |