Papers by Manish Shrivastava

Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data (P19-2)

Challenge:	In this paper, we address the problem of the automatic generation of natural language text representative of an input non-linguistic collection of knowledge.
Approach:	They propose a fitness function which is used to generate grammatical sentences . they use an evolutionary algorithm to model the fitness of the sentence .
Outcome:	The proposed approach uses an evolutionary algorithm to arrive at a potentially grammatical sentence which satisfies the input constraints.

Copied to clipboard

Vamshi Krishna Srirangam, Appidi Abhinav Reddy, Vinay Singh, Manish Shrivastava

Information Extraction & Text Mining

Challenge:	Named Entity Recognition (NER) is a subtask of Information Extraction in NLP.
Approach:	They present a Telugu-English code-mixed corpus with the corresponding named entity tags.
Outcome:	The proposed model scored 0.96, 0.94 and 0.95 on a Telugu-English code-mixed corpus.

Automatic Question Generation using Relative Pronouns and Adverbs (P18-3)

Copied to clipboard

Payal Khullar, Konigari Rachna, Mukul Hase, Manish Shrivastava

TeSum: Human-Generated Abstractive Summarization Corpus for Telugu (2022.lrec-1)

Challenge:	Automatic Question Generation is a system that generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences.
Approach:	They propose a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences.
Outcome:	The proposed system generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences.

Copied to clipboard

Ashok Urlana, Nirmal Surange, Pavan Baswani, Priyanka Ravva, Manish Shrivastava

Diverse Multi-Answer Retrieval with Determinantal Point Processes (2022.coling-1)

Challenge:	a number of recent datasets for summarisation, scraped the web-content relying on the assumption that summary is made available with the article by the publishers.
Approach:	They propose a pipeline that crowd-sources summarization data and then aggressively filters the content via: automatic and partial expert evaluation.
Outcome:	The proposed pipeline can be applied to scraped datasets to extract better quality articles-summaries pairs.

Copied to clipboard

Poojitha Nandigam, Nikhil Rayaprolu, Manish Shrivastava

TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu (2024.lrec-main)

Challenge:	Existing open domain question answering systems provide a single answer to ambiguous questions.
Approach:	They propose a re-ranking approach that takes query-passage relevance and passage-passance correlation into account to retrieve passages that are query-relevant and diverse.
Outcome:	The proposed method outperforms state-of-the-art on the AmbigQA dataset.

Copied to clipboard

Gopichand Kanumolu, Lokesh Madasu, Nirmal Surange, Manish Shrivastava

DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection (2022.emnlp-main)

Challenge:	Relevance-based headline classification is under-explored in low-resource languages like Telugu due to a lack of annotated data.
Approach:	They propose that relevance-based headline classification can greatly aid the task of generating relevant headlines.
Outcome:	The proposed model can generate relevant headlines with 78,534 annotations in Telugu . the model shows a 5 point increment in the ROUGE-L scores .

Copied to clipboard

Puneet Mathur, Gautam Kunapuli, Riyaz Bhat, Manish Shrivastava, Dinesh Manocha, Maneesh Singh

Reasoning

Challenge:	Documentlevel NLI is an important problem for many tasks including verification of factual correctness of documents.
Approach:	They propose a document-level natural language inference model that builds a hierarchical document graph enriched through inter-sentence relations and performs paragraph pruning using the novel SubGraph Pooling layer.
Outcome:	The proposed model performs on a legal judicial reasoning task with a dataset enriched with document graphs and a proposed evidence selection algorithm.

Analyzing (In)Abilities of SAEs via Formal Languages (2025.naacl-long)

Copied to clipboard

Abhinav Menon, Manish Shrivastava, David Krueger, Ekdeep Singh Lubana

Explainability & Interpretability in NLP

Challenge:	Autoencoders have been used for finding interpretable and disentangled features underlying neural network representations in both image and text domains, but there is a lack of corresponding results for the text domain.
Approach:	They propose to train sparse autoencoders (SAEs) on a synthetic testbed of formal languages to find interpretable latents in models trained on formal languages.
Outcome:	The proposed approach promotes learning of causally relevant features in a formal language setting.

De-Mixing Sentiment from Code-Mixed Text (P19-2)

Copied to clipboard

Yash Kumar Lal, Vaibhav Kumar, Mrinal Dhar, Manish Shrivastava, Philipp Koehn

Semantic Text Processing

Challenge:	Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence.
Approach:	They propose a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data using CNNs to generate subword representations for the sentences.
Outcome:	The proposed architecture achieves 83.54% accuracy and 0.827 F1 score on a benchmark dataset.

Knowledge-Aware Reasoning over Multimodal Semi-structured Tables (2024.findings-emnlp)

Copied to clipboard

Suyash Mathur, Jainit Bafna, Kunal Kartik, Harshita Khandelwal, Manish Shrivastava, Vivek Gupta, Mohit Bansal, Dan Roth

AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts (2020.coling-main)

Challenge:	Existing datasets for tabular question answering focus on text within cells, but real-world data is multimodal, often blending images such as symbols, faces, icons, patterns, and charts with textual content.
Approach:	They propose a dataset to assess whether current AI models can perform knowledge-aware reasoning on multimodal structured data.
Outcome:	The proposed dataset is a robust benchmark for advancing AI’s comprehension and capabilities in analyzing multimodal structured data.

Copied to clipboard

Mohit Chandra, Ashwin Pathak, Eesha Dutta, Paryul Jain, Manish Gupta, Manish Shrivastava, Ponnurangam Kumaraguru

Responsible & Trustworthy NLP

Challenge:	Existing studies on estimating the severity of abuse and the target of online abuse have focused on detecting and curtailment of such types of abuse.
Approach:	They propose to analyze online abuse from the perspective of presence, severity and target of abusive behavior from 7,601 posts from Gab and to estimate the severity of abuse.
Outcome:	The proposed system achieves 80% accuracy for abuse presence, 82% accuracy for abusive target prediction, and 65% accuracy for severity prediction.

Leveraging Data Recasting to Enhance Tabular Reasoning (2022.findings-emnlp)

Copied to clipboard

Aashna Jena, Vivek Gupta, Manish Shrivastava, Julian Eisenschlos

Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System (L18-1)

Challenge:	Existing approaches to create tabular inference data are limited by human annotation and synthetic generation.
Approach:	They propose a framework for semi-automatically recasting tabular data to make use of both approaches.
Outcome:	The proposed framework can be used to build tabular NLI instances from five datasets.

Copied to clipboard

Ankush Khandelwal, Sahil Swami, Syed S. Akhtar, Manish Shrivastava

Text Classification

Challenge:	a growing number of social media users are using code-mixing to detect humor . linguistics researchers are looking for methods to detect humorous content in text .
Approach:	They analyze a corpus of English-Hindi code-mixed tweets annotated with humorous(H) tags.
Outcome:	The proposed method detects humor in code-mixed tweets in English-Hindi.

SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages (2024.findings-acl)

Copied to clipboard

Nedjma Ousidhoum, Shamsuddeen Muhammad, Mohamed Abdalla, Idris Abdulmumin, Ibrahim Ahmad, Sanchit Ahuja, Alham Aji, Vladimir Araujo, Abinew Ayele, Pavan Baswani, Meriem Beloucif, Chris Biemann, Sofia Bourhim, Christine Kock, Genet Dekebo, Oumaima Hourrane, Gopichand Kanumolu, Lokesh Madasu, Samuel Rutunda, Manish Shrivastava, Thamar Solorio, Nirmal Surange, Hailegnaw Tilaye, Krishnapriya Vishnubhotla, Genta Winata, Seid Yimam, Saif Mohammad

Semantic Similarity

Challenge:	SemRel datasets are annotated by native speakers across 13 languages . they are used to characterise the relationship between two units of text .
Approach:	They propose to use a semantic relatedness dataset to measure the degree of semantic textual relatedness between sentences in Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish, and Telugu.
Outcome:	The proposed datasets are annotated by native speakers across 13 languages and represent the semantic relatedness of 13 languages.

X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents (2023.findings-acl)

Copied to clipboard

Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gael de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica Lam

Multilinguality

Challenge:	X-RiSAWOZ dataset has more than 18,000 human-verified dialogue utterances for each language . Xiaoping and Xinhui are the main challenges for task-oriented dialogue research .
Approach:	They develop a toolkit to accelerate the post-editing of a new language dataset after translation . their dataset, code, and toolkit are released open-source .
Outcome:	The proposed toolkit accelerates the post-editing of a new language dataset after translation.

Bilingual Tabular Inference: A Case Study on Indic Languages (2022.naacl-main)

Copied to clipboard

Chaitanya Agarwal, Vivek Gupta, Anoop Kunchukuttan, Manish Shrivastava

Multilinguality

Challenge:	Existing studies on Tabular Natural Language Inference (TNLI) focus on monolingual settings where tabular premise and hypothesis are in the same language.
Approach:	They propose a task where tabular premise and hypothesis are in two languages . they translate textual hypotheses from an English-indic TNLI dataset into eleven major languages - english and indic .
Outcome:	The proposed model performs well on a bilingual dataset in English and in 11 major Indian languages.

Answering Naturally: Factoid to Full length Answer Generation (D19-54)

Copied to clipboard

Vaishali Pal, Manish Shrivastava, Irshad Bhat

Natural Language Interfaces

Challenge:	Factoid question answering systems extract answers for a question from passages, which are usually short spans of text . but, these spans would result in an unnatural reading experience in a conversational system . a pointer generator based full-length answer generator can be used with most QA systems .
Approach:	They propose a pointer generator based full-length answer generator which can be used with most QA systems.
Outcome:	The proposed system generates full length answer without relying on passage from which it was extracted.

SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing (2022.findings-acl)

Copied to clipboard

Prashant Kodali, Anmol Goel, Monojit Choudhury, Manish Shrivastava, Ponnurangam Kumaraguru

Syntactic Text Processing

Challenge:	Recent work on code mixing in computational settings has leveraged social media code mixed texts to train NLP models.
Approach:	They propose to use language ID tags to measure syntactic variety in code-mixed text and their relationship with computational model performance.
Outcome:	The proposed measure can be applied to English(en)-hindi(hi) code-mixed datasets and compares them with other measures.

Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction (C18-1)

Copied to clipboard

Nurendra Choudhary, Rajat Singh, Vijjini Anvesh Rao, Manish Shrivastava

SCAR: Sentence Compression using Autoencoders for Reconstruction (2020.acl-srw)

Challenge:	a majority of research studies on twitter focus on English tweets, despite the fact that English dominates the mix of languages.
Approach:	They leverage social media platforms such as twitter for developing corpus across multiple languages . they use tweets to collect data for sentiment analysis and emoji prediction .
Outcome:	The proposed method is applicable for resource-scarce languages provided speakers of that particular language are active users on social media platforms.

Copied to clipboard

Chanakya Malireddy, Tirth Maniar, Manish Shrivastava

Language Models

Challenge:	Sentence compression is the task of shortening a sentence while retaining its meaning.
Approach:	They propose to use a supervised deep learning framework to shorten sentences while retaining their meaning by a compressor and reconstructor.
Outcome:	The proposed model achieves higher ROUGE scores on benchmark datasets than the existing state-of-the-art methods and baselines.

Towards Fine-grained Classification of Climate Change related Social Media Text (2022.acl-srw)

Copied to clipboard

Roopal Vaid, Kartikey Pant, Manish Shrivastava

Information Extraction & Text Mining

Challenge:	a new study examines the fine-grained classification and classification of climate change-related social media text.
Approach:	They propose to use two datasets to analyze climate change-related social media text and propose a fine-grained classification based on the proposed dataset.
Outcome:	The proposed datasets are compared with existing datasets and benchmarked using the best-performing model.

Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction (2020.coling-main)

Copied to clipboard

Abhinav Reddy Appidi, Vamshi Krishna Srirangam, Darsi Suhas, Manish Shrivastava

Sentiment Analysis

Challenge:	Existing work on emotion prediction for resource-rich languages has focused on code-mixed social media corpus but not on Kannada-English code-mixed Twitter data.
Approach:	They analyze Kannada-English code-mixed Twitter corpus annotated with their respective ‘Emotion’ for each tweet.
Outcome:	The proposed model based on Kannada-English code-mixed Twitter corpus yielded an accuracy of 30% and 32% respectively.

MAPWise: Evaluating Vision-Language Models for Advanced Map Queries (2025.naacl-long)

Copied to clipboard

Srija Mukhopadhyay, Abhishek Rajgaria, Prerana Khatiwada, Manish Shrivastava, Dan Roth, Vivek Gupta

Natural Language Interfaces

Challenge:	Vision-language models excel at tasks requiring joint understanding of visual information and natural language.
Approach:	They propose to use choropleth maps to answer questions from three geographical regions in the United States, India, China as question templates.
Outcome:	The proposed model outperforms other models in the area of visual language and visual question answering.

Corpus Creation and Emotion Prediction for Hindi-English Code-Mixed Social Media Text (N18-4)

Copied to clipboard

Deepanshu Vijay, Aditya Bohra, Vinay Singh, Syed Sarfaraz Akhtar, Manish Shrivastava

Universal Dependency Parsing for Hindi-English Code-Switching (N18-1)

Challenge:	Emotion Prediction is a natural language processing task dealing with detection and classification of emotions in monolingual and bilingual texts.
Approach:	They propose a machine learning system which uses various machine learning techniques to detect emotion associated with tweets.
Outcome:	The proposed system uses various machine learning techniques to detect emotion associated with the text.

Copied to clipboard

Irshad Bhat, Riyaz A. Bhat, Manish Shrivastava, Dipti Sharma

Syntactic Text Processing

Challenge:	Code-switching data often need additional processes such as language identification, normalization and/or back-transliteration to be processed.
Approach:	They propose a neural stacking model that leverages part-of-speech tags and syntactic tree annotations in tweets to parse code-switching data.
Outcome:	The proposed model is 1.5% better than the augmented model and 3.8% better than one which uses first-best normalization and/or back-transliteration.

Predicting Algorithm Classes for Programming Word Problems (D19-55)

Copied to clipboard

Vinayak Athavale, Aayush Naik, Rajas Vanjape, Manish Shrivastava

Is My Model Using the Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning (2022.tacl-1)

Challenge:	Using a text classification problem, we map programming word problems to relevant classes of algorithms.
Approach:	They propose to map programming word problems to relevant classes of algorithms by using a text classification problem as a classification task.
Outcome:	The proposed algorithm class prediction is 9 percent lower than a human on the task.

Copied to clipboard

Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Shrivastava, Maneesh Singh, Vivek Srikumar

A Pregroup Representation of Word Order Alternation Using Hindi Syntax (N19-3)

Challenge:	Existing models that claim to reason about evidence should avoid spurious patterns . tabular inputs are well-suited for the study—they admit systematic probes .
Approach:	They propose to use tabular data to test whether models can reason about evidence . they show that a RoBERTa-based model fails to reason on the following counts .
Outcome:	The proposed model fails to reason on tabular data on the following counts . the model is over-sensitive to annotation artifacts and ignores relevant parts of the evidence .

Copied to clipboard

Alok Debnath, Manish Shrivastava

Representation Learning

Challenge:	Existing methods for expressing restricted word order alternation have not been used for word order representations.
Approach:	They propose three methods to represent restricted word order alternation in the pregroup representation of any language.
Outcome:	The proposed methods are able to represent word alternation in English using the example of Hindi syntax.

Why should only High-Resource-Languages have all the fun? Pivot Based Evaluation in Low Resource Setting (2025.coling-main)

Copied to clipboard

Ananya Mukherjee, Saumitra Yadav, Manish Shrivastava

Multilinguality

Challenge:	a limited number of evaluation metrics and resources are available for low-resource languages . a pivot-based evaluation framework is proposed to address these limitations .
Approach:	They propose a pivot-based evaluation framework that leverages advanced metrics for more meaningful evaluation.
Outcome:	The proposed framework extends the coverage of both lexical-based and embedding-based metrics even for languages not directly supported by advanced metrics.

PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India (2023.findings-emnlp)

Copied to clipboard

Ashok Urlana, Pinzhen Chen, Zheng Zhao, Shay Cohen, Manish Shrivastava, Barry Haddow

TabXEval: Why this is a Bad Table? An eXhaustive Rubric for Table Evaluation (2025.findings-acl)

Challenge:	Existing datasets for Indian languages are limited in terms of coverage and size.
Approach:	They propose a multilingual and massively parallel summarization corpus focused on languages in India that provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs.
Outcome:	The proposed dataset provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs.

Copied to clipboard

Vihang Pancholi, Jainit Sushil Bafna, Tejas Anvekar, Manish Shrivastava, Vivek Gupta