Papers by Parag Singla
Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion (2023.acl-short)
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| Challenge: | Recent studies show that high-quality rule sets struggle with high coverage. |
| Approach: | They propose three simple augmentations to existing rule sets to improve results . they propose transforming rules to their abductive forms and generating equivalent rules that use inverse forms of constituent relations . |
| Outcome: | The proposed methods achieve up to 7.1 pt MRR and 8.5 pT Hits@1 gains over using rules without augmentations. |
Image Manipulation via Multi-Hop Instructions - A New Dataset and Weakly-Supervised Neuro-Symbolic Approach (2023.emnlp-main)
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Harman Singh, Poorva Garg, Mohit Gupta, Kevin Shah, Ashish Goswami, Satyam Modi, Arnab Mondal, Dinesh Khandelwal, Dinesh Garg, Parag Singla
| Challenge: | Recent studies have shown that neuro-symbolic models lack interpretability and are not robust to noise. |
| Approach: | They propose to extend Neuro Symbolic Concept Learning (NSCL) which has been quite effective for the task of Visual Question Answering (VQA) they create a new dataset for the image manipulation task and demonstrate that NeuroSIM is highly competitive with or beats SOTA baselines that make use of supervised data for manipulation. |
| Outcome: | The proposed system performs complex multi-hop reasoning over multi-object scenes and only requires weak supervision in the form of annotated data for VQA. |
Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction (2026.acl-long)
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| Challenge: | Existing Distantly Supervised Relation Extraction models rely on task-specific training, but their integration with in-context learning (ICL) using large language models (LLMs) remains underexplored. |
| Approach: | They propose a framework for distantly supervised relation extraction that uses a trained DSRE model to identify the top-k candidate relations for a given test sentence and a dynamic exemplar retrieval strategy that extracts reliable, sentence-level exemplars from training data. |
| Outcome: | The proposed framework achieves 20 F1 points gains in English and 17 F1 point gains on Indic languages over previous models and naive prompting baselines. |
DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion (2024.acl-short)
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| Challenge: | Existing approaches to Knowledge Graph Completion use textual descriptions of the KG entities and relations to perform the task. |
| Approach: | They propose a method to combine two popular approaches to Knowledge Graph Completion . structure-based models perform better when gold answer is easily reachable . textual models exploit textual descriptions to give good performance . |
| Outcome: | The proposed method achieves 6.8 pt MRR and 8.3 pTits@1 gains over the best baseline model for WN18RR dataset. |
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. |
Explanations for CommonsenseQA: New Dataset and Models (2021.acl-long)
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Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, Dinesh Garg
| Challenge: | a dataset called CommonsenseQA (CQA) was recently released to advance the research on common-sense question answering (QA) |
| Approach: | They propose to retrieve and generate explanations for a given question, correct answer choice, incorrect answer choices tuple from a dataset called CommonsenseQA. |
| Outcome: | The proposed model beats baseline model by 100% in F1 score and similarity score of 61.9 . |
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. |
LLMs are Brittle to Simple Code Transformations: Introducing CETBench – A Benchmark for Code-Equivalence Checking (2026.findings-acl)
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| Challenge: | a new benchmarking tool for code equivalence checks the performance of LLMs. |
| Approach: | They propose a code-equivalence with transformations benchmark built from a repository of programs that may solve the same or different tasks. |
| Outcome: | The proposed approach boosts performance on the transformed pairs of programs. |