1 citations · 2 across the 6 of their papers we have counts for
7 papers
Prompting with Pseudo-Code Instructions
Mayank Mishra, Prince Kumar, Riyaz Bhat +3
Prompting with natural language instructions has recently emerged as a popular method of harnessing the capabilities of large language models. Given the inherent ambiguity present…
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
Avirup Sil, Jaydeep Sen, Bhavani Iyer +12
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets wit…
Semi-Structured Object Sequence Encoders
Rudra Murthy, Riyaz Bhat, Chulaka Gunasekara +5
In this paper we explore the task of modeling semi-structured object sequences; in particular, we focus our attention on the problem of developing a structure-aware input represent…
Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning
Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal +3
Neural models command state-of-the-art performance across NLP tasks, including ones involving "reasoning". Models claiming to reason about the evidence presented to them should att…
Leveraging Newswire Treebanks for Parsing Conversational Data with Argument Scrambling
Riyaz Ahmad Bhat, Irshad Ahmad Bhat, Dipti Misra Sharma
We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently. We evaluate a state-of-the-ar…
Universal Dependency Parsing for Hindi-English Code-switching
Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava +1
Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the bench…
Joining Hands: Exploiting Monolingual Treebanks for Parsing of Code-mixing Data
Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava +1
In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather the…