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Dinesh Garg

8 accepted papers

2025

Transforming Code Understanding: Clustering-Based Retrieval for Improved Summarization in Domain-Specific Languages

Baban Gain, Dibyanayan Bandyopadhyay, Samrat Mukherjee, Aryan Sahoo, Saswati Dana, Palanivel Kodeswaran +3
COLING 2025industry

A domain-specific extension of C language known as extended Berkeley Packet Filter (eBPF) has gained widespread acceptance for various tasks, including observability, security, and network acceleration in the cloud community. Due to its recency and complexity, there is an overwhelming need for natur…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

Read between the lines - Functionality Extraction From READMEs

Prince Kumar, Srikanth Tamilselvam, Dinesh Garg
NAACL 2024findings

While text summarization is a well-known NLP task, in this paper, we introduce a novel and useful variant of it called functionality extraction from Git README files. Though this task is a text2text generation at an abstract level, it involves its own peculiarities and challenges making existing tex…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2023

Image Manipulation via Multi-Hop Instructions - A New Dataset and Weakly-Supervised Neuro-Symbolic Approach

Harman Singh, Poorva Garg, Mohit Gupta, Kevin Shah, Ashish Goswami, Satyam Modi +4
EMNLP 2023long main

We are interested in image manipulation via natural language text -- a task that is useful for multiple AI applications but requires complex reasoning over multi-modal spaces. We extend recently proposed Neuro Symbolic Concept Learning (NSCL), which has been quite effective for the task of Visual Qu…

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2022

SYGMA: A System for Generalizable and Modular Question Answering Over Knowledge Bases

Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz +19
EMNLP 2022finding

Knowledge Base Question Answering (KBQA) involving complex reasoning is emerging as an important research direction. However, most KBQA systems struggle with generalizability, particularly on two dimensions: (a) across multiple knowledge bases, where existing KBQA approaches are typically tuned to a…

Cited by 24Source ↗PDF ↗code ↗Scholar ↗
2022

Zero-shot Entity Linking with Less Data

G P Shrivatsa Bhargav, Dinesh Khandelwal, Saswati Dana, Dinesh Garg, Pavan Kapanipathi, Salim Roukos +2
NAACL 2022findings

Entity Linking (EL) maps an entity mention in a natural language sentence to an entity in a knowledge base (KB). The Zero-shot Entity Linking (ZEL) extends the scope of EL to unseen entities at the test time without requiring new labeled data. BLINK (BERT-based) is one of the SOTA models for ZEL. In…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2021

Explanations for CommonsenseQA: New Dataset and Models

Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, Dinesh Garg
ACL 2021long

CommonsenseQA (CQA) (Talmor et al., 2019) dataset was recently released to advance the research on common-sense question answering (QA) task. Whereas the prior work has mostly focused on proposing QA models for this dataset, our aim is to retrieve as well as generate explanation for a given (questio…

Cited by 156Source ↗PDF ↗code ↗Scholar ↗
2020

Inductive Quantum Embedding

Santosh Kumar Srivastava, Dinesh Khandelwal, Dhiraj Madan, Dinesh Garg, Hima Karanam, L Venkata Subramaniam
NeurIPS 2020poster

Quantum logic inspired embedding (aka Quantum Embedding (QE)) of a Knowledge-Base (KB) was proposed recently by Garg:2019. It is claimed that the QE preserves the logical structure of the input KB given in the form of unary and binary predicates hierarchy. Such structure preservation allows one to p…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2019

Quantum Embedding of Knowledge for Reasoning

Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L Venkata Subramaniam
NeurIPS 2019poster

Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs). These methods embed any given KB into a vector space by exploiting statistical similarities among its entities and predicates but withou…

Cited by 66Source ↗PDF ↗code ↗Scholar ↗