Papers by Vishwajeet Kumar
Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5 (2025.naacl-long)
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| Challenge: | Existing benchmarks for evaluating retrieval models in Hindi are lacking . despite efforts to build multilingual retrieval systems, this is still a work in progress . |
| Approach: | They evaluate Hindi retrieval models on the Hindi-BEIR benchmark and introduce a multilingual model that leverages a zero-shot approach to support Hindi without the need for Hindi training data. |
| Outcome: | The proposed model leverages a zero-shot approach to support Hindi without the need for Hindi training data. |
Cross-Lingual Training for Automatic Question Generation (P19-1)
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| Challenge: | Automatic question generation is a challenging problem in natural language understanding . manual curating a dataset of comparable size for a new language is tedious and expensive. |
| Approach: | They propose to reuse available large QG dataset in a secondary language to learn a QG model for a primary language. |
| Outcome: | The proposed model outperforms baseline models in Hindi and Chinese. |
MILU: A Multi-task Indic Language Understanding Benchmark (2025.naacl-long)
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| Challenge: | Existing benchmarks focus on English, leaving substantial gaps in assessing LLM capabilities in low-resource and linguistically diverse languages. |
| Approach: | They propose a multi-task indic language understanding benchmark to assess LLMs in low-resource languages. |
| Outcome: | The new benchmark spans 8 domains and 41 subjects across 11 Indic languages, reflecting general and culturally specific knowledge. |
WARM: A Weakly (+Semi) Supervised Math Word Problem Solver (2022.coling-1)
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| Challenge: | Existing approaches to solving math word problems require full supervision in the form of intermediate equations. |
| Approach: | They propose a weakly supervised model that requires only the final answer as supervision to solve math word problems. |
| Outcome: | The proposed model achieves accuracy gains of 4.5% and 32% over current weakly-supervised methods on standard Math23K and AllArith datasets. |
On Utilizing Constituent Language Resources to Improve Downstream Tasks in Hinglish (2022.findings-emnlp)
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| Challenge: | Intuitively, Hindi and English corpora should aid improve task performance on code-switched Hindi-English. |
| Approach: | They propose a meta-learning framework that utilizes the labelled resources of the downstream tasks in the constituent languages to improve task performance. |
| Outcome: | The proposed framework improves the performance on downstream tasks on code-switched Hindi-English. |
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry (2022.naacl-industry)
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Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
| Challenge: | Table Question Answering (Table QA) systems have been shown to be highly accurate when trained and tested on open-domain datasets built on top of Wikipedia tables. |
| Approach: | They propose a domain-specific Table QA test dataset to test Table Question Answering systems on open-domain datasets built on top of Wikipedia tables. |
| Outcome: | The proposed methods are highly accurate when tested on open-domain datasets built on top of Wikipedia tables. |
Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering (2023.acl-long)
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Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath, Jaydeep Sen, Soumen Chakrabarti, Samarth Bharadwaj, Feifei Pan
| Challenge: | Existing question answering systems for tables and linked text are relatively unexplored. |
| Approach: | They propose a transformer-based question answering system that copes with distant supervision along both axes of the question and answer. |
| Outcome: | The proposed system beats baselines for HybridQA and OTT-QA with best EM and F1 scores on a held out test set. |
Topic Transferable Table Question Answering (2021.emnlp-main)
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Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Jaydeep Sen, Mustafa Canim, Soumen Chakrabarti, Alfio Gliozzo, Karthik Sankaranarayanan
| Challenge: | Weakly-supervised table question-answering (TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table to produce structured query for the question. |
| Approach: | They propose a framework for TableQA that incorporates topic-specific vocabulary injection into BERT, a novel text-to-text transformer generator and a logical form re-ranker. |
| Outcome: | The proposed framework provides a reasonably good baseline for topic shift benchmarks. |
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)
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Avi Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Juergen Bross, Radu Florian, Salim Roukos
| Challenge: | Question Answering (QA) is a major area of research in Natural Language Processing (NLP) |
| Approach: | They propose a one-stop and open-source QA repository for question answering . it supports core QA functionalities like retrieval and reading comprehension . they say it will facilitate easy replication of state-of-the-art (SOTA) QA methods . |
| Outcome: | The proposed framework enables easy replication of state-of-the-art (SOTA) QA methods. |
Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data (N18-5)
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| Challenge: | Existing systems for resolving entities and disambiguating locations based on publicly available web data are challenging because of the limited information available on the Web. |
| Approach: | They propose a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples. |
| Outcome: | The proposed system resolves entities and disambiguates locations with high confidence using grammar rules and clustering algorithms. |
ODASim: Ordered, Distinctive and Absolute Semantic Similarity for Code Explanation Evaluation (2026.findings-acl)
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Prince Kumar, Vitobha Munigala, Jaydeep Sen, Ashish Mittal, Vishwajeet Kumar, Srikanth G. Tamilselvam
| Challenge: | Existing methods for code explanations fail to distinguish correct from partially or fully incorrect explanations and their similarity scores are poorly calibrated. |
| Approach: | They propose a model-agnostic graded fine-tuning framework that learns calibrated similarity representations between code and explanations to support fine-grained supervision and evaluation. |
| Outcome: | The proposed framework improves F1 score and ECE scores on two embedding models and reduces expected calibration error. |
Meta-Learning for Effective Multi-task and Multilingual Modelling (2021.eacl-main)
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| Challenge: | Existing studies on multitask and multilingual learning have shown that learning cross-lingual embeddings can benefit multiple tasks and languages. |
| Approach: | They propose a meta-learning approach to learn interactions between tasks and languages . they also investigate the role of different sampling strategies used during meta-learned model . |
| Outcome: | The proposed model improves on five different tasks and six different languages from the XTREME multilingual benchmark dataset. |
Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation (2020.aacl-main)
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| Challenge: | Current neural network-based questions generation techniques take only one or two sentences as input. |
| Approach: | They propose a simple yet effective technique for question generation from paragraphs . they augment a sequence-to-sequence QG model with dynamic, paragraph-specific dictionary . |
| Outcome: | The proposed model outperforms state-of-the-art systems in question generation from paragraphs in automatic and human evaluation. |
ParaQG: A System for Generating Questions and Answers from Paragraphs (D19-3)
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| Challenge: | Automated question generation systems generate questions from sentences and paragraphs . manual generation of questions is labour-intensive as it requires reading, parsing and understanding of long passages of text. |
| Approach: | They propose a web-based system for generating questions from sentences and paragraphs . paraQG provides an interactive interface for a user to select answers with visual insights . |
| Outcome: | The proposed system generates questions from sentences and paragraphs on a web-based platform. |
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages (2025.findings-naacl)
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Abhishek Kumar Singh, Vishwajeet Kumar, Rudra Murthy, Jaydeep Sen, Ashish Mittal, Ganesh Ramakrishnan
| Challenge: | Large Language Models perform well on unseen tasks in English, but their abilities in non-English languages are less explored due to limited benchmarks and training data. |
| Approach: | They propose to release a large dataset for context-grounded question answering in 11 major Indian languages. |
| Outcome: | The Indic-QA Benchmark compared large datasets of large LLMs on extractive and abstractive tasks in 11 major Indian languages. |
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables (2021.naacl-main)
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Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti, Avi Sil, Feifei Pan, Samarth Bharadwaj, Nicolas Rodolfo Fauceglia
| Challenge: | Existing transformer based approaches have been used to answer questions over tables. |
| Approach: | They propose a transformer based architecture that independently classifies rows and columns to identify relevant cells and a model that incorporates existing tables to improve efficiency. |
| Outcome: | The proposed model outperforms the state-of-the-art transformer based approaches on WikiSQL lookup questions and achieves 3.4% and 18.86% additional precision improvement on the standard WikisQL benchmark. |