Papers by Vipul Rathore
PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction (2022.acl-short)
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| Challenge: | Recent approaches to distantly supervised relation extraction (DS-RE) encode each sentence in an entity-pair bag separately. |
| Approach: | They propose a simple baseline approach where sentences of a bag are concatenated into a passage of sentences and encoded jointly using BERT. |
| Outcome: | The proposed approach outperforms state-of-the-art models in monolingual and multilingual datasets. |
SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models (2024.findings-emnlp)
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| Challenge: | Recent studies have shown that very large language models (LLMs) can perform NLP tasks with just in-context learning (ICL) but their utility in other languages is underexplored. |
| Approach: | They propose a novel approach to in-context learning that uses noisy test data to generate more accurate labels for LLMs. |
| Outcome: | Experiments on three tasks and eleven LLMs show that the proposed approach outperforms existing in-context learning baselines on English NLP and reasoning tasks. |
ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language Adapters (2023.emnlp-main)
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| Challenge: | Existing approaches to zero-shot cross-lingual transfer have focused on training with adapters of a single source and testing either with the target LA or LA of another related language. |
| Approach: | They propose to leverage LAs of multiple (linguistically or geographically related) source languages for more effective cross-lingual transfer instead of just one source LA . they extend their novel neural architecture, ZGUL, to settings where either (1) some unlabeled data or (2) few-shot training examples are available for the target language . |
| Outcome: | Extensive experimentation across four language groups, covering 15 unseen target languages, shows improvements of up to 3.2 average F1 points over baselines on POS tagging and NER tasks. |
IMoJIE: Iterative Memory-Based Joint Open Information Extraction (2020.acl-main)
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| Challenge: | Recent neural OpenIE systems are statistical or rule-based for Open Information Extraction. |
| Approach: | They propose an extension to CopyAttention that produces the next extraction conditioned on all previously extracted tuples. |
| Outcome: | The proposed model outperforms CopyAttention by 18 pts and a BERT-based strong baseline by 2 ptes. |