Papers by Rahul Kumar
From Laughter to Inequality: Annotated Dataset for Misogyny Detection in Tamil and Malayalam Memes (2024.lrec-main)
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Rahul Ponnusamy, Kathiravan Pannerselvam, Saranya R, Prasanna Kumar Kumaresan, Sajeetha Thavareesan, Bhuvaneswari S, Anshid K.a, Susminu S Kumar, Paul Buitelaar, Bharathi Raja Chakravarthi
| Challenge: | a new form of memes has emerged to combat misogyny and harmful stereotypes . authors present a dataset to analyze online misogamy in Tamil and Malayalam communities . |
| Approach: | They propose to create an annotated dataset with detailed annotation guidelines to analyze online misogyny within Tamil and Malayalam-speaking communities. |
| Outcome: | The proposed dataset reveals the world of gender bias and stereotypes in Tamil and Malayalam-speaking communities. |
On Localizing and Deleting Toxic Memories in Large Language Models (2025.findings-naacl)
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Anubrata Das, Manoj Kumar, Ninareh Mehrabi, Anil Ramakrishna, Anna Rumshisky, Kai-Wei Chang, Aram Galstyan, Morteza Ziyadi, Rahul Gupta
| Challenge: | Existing methods to reduce toxic generation in large language models are not fully understood. |
| Approach: | They propose to understand the mechanisms that drive toxic generation in large language models by using memory localization to reduce toxic generation. |
| Outcome: | The proposed method reduces toxic generation from 62.86% to 28.61%, but it also improves generation quality. |
MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines (2020.lrec-1)
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Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao, Adarsh Kumar, Anuj Goyal, Peter Ku, Dilek Hakkani-Tur
| Challenge: | MultiWOZ 2.0 has substantial noise in dialogue state annotations and dialogue utterances . follow-up work has augmented the original dataset with user dialogue acts . |
| Approach: | They propose to reannotate dialogue state and utterances based on original dataset . they then compare their results to other datasets to improve their models . |
| Outcome: | The proposed dataset improves on the noise in the dialogue state annotations and dialogue utterances. |
Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays (2022.naacl-main)
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| Challenge: | In automatic essay grading, essay traits are important for scoring the essay holistically . a single-task learning system gives the best results for scoring essays holistically and scoring essay traits. |
| Approach: | They propose a way to score essays using a multi-task learning approach . they compare the MTL-based BiLSTM system to a single-task Learning approach based on LSTMs and BiLStms . |
| Outcome: | The proposed system gives better results for scoring essay holistically and scoring essay traits. |
On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations (2022.acl-short)
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Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, Aram Galstyan
| Challenge: | Recent natural language processing systems use large language models as the backbone . however, societal biases are encoded in these models and transferred to downstream applications . |
| Approach: | They propose to use two categories to measure fairness in natural language processing tasks . they find intrinsic and extrinsic metrics do not correlate in their original setting . |
| Outcome: | The proposed metrics do not correlate in their original setting, the authors show . they find that they are not accurate when correcting for metric misalignments and noise . |
Model-agnostic Methods for Text Classification with Inherent Noise (2020.coling-industry)
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| Challenge: | Text classification is a fundamental problem in natural language processing, but its performance relies on high-quality annotations. |
| Approach: | They propose to use model-agnostic methods to handle inherent noise in large scale text classification that can be easily incorporated into existing machine learning workflows with minimal interruption. |
| Outcome: | The proposed method outperforms baselines by up to 10% in classification accuracy while requiring no network modifications. |
Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages (2023.acl-long)
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Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, Shreya Goyal, Mitesh M. Khapra, Anoop Kunchukuttan, Pratyush Kumar
| Challenge: | Recent advances in Natural Language Understanding are driven by pretrained multilingual models, which can potentially reduce the performance gap between high-resource languages through zero-shot knowledge transfer. |
| Approach: | They propose to create a human-supervised benchmark for Indic languages, IndicXTREME, with nine diverse NLU tasks covering 20 languages. |
| Outcome: | The proposed model improves on the monolingual corpora, IndicCorp, and IndicBERT in Indic languages with 105 evaluation sets across languages and tasks. |
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning (2024.findings-emnlp)
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Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey, Alexandre Rame, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Leonard Hussenot, Olivier Bachem, Edouard Leurent
| Challenge: | Existing approaches for multi-objective Reinforcement Learning (RL) are difficult due to plurality of preferences and applications. |
| Approach: | They propose a framework for finetuning language models on multiple objectives using conditional language policy. |
| Outcome: | The proposed framework outperforms and Pareto-dominates existing approaches for multi-objective Reinforcement Learning (RL) it does not require training or maintaining multiple models to achieve different trade-offs between the objectives. |
NLMs: Augmenting Negation in Language Models (2023.findings-emnlp)
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| Challenge: | Negation is the fundamental component in a natural language that reverses the semantic meaning of a sentence. |
| Approach: | They propose a language model objective with a weighted cross-entropy loss and elastic weight consolidation regularization to improve negation understanding. |
| Outcome: | The proposed model reduces the error rate of the existing models by 8% and outperforms them on original and negation benchmarks. |
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal (2022.findings-acl)
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Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, Aram Galstyan
| Challenge: | Language models excel at generating coherent text, but can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. |
| Approach: | They propose to modify teacher probabilities and augment the training set to learn a fair model during knowledge distillation by modifying teacher probability and augmenting the training sets. |
| Outcome: | The proposed approach reduces gender disparity in open-ended text generated from the distilled and finetuned models with only a minor compromise in utility. |
Resolving Ambiguities in Text-to-Image Generative Models (2023.acl-long)
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Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Rahul Gupta
| Challenge: | ambiguities can lead to misinterpretation and miscommunication in natural language . resolving ambiguity is notoriously hard for machines . |
| Approach: | They propose a framework to disambiguate prompts given to generative models by soliciting clarifications from the end user. |
| Outcome: | The proposed framework generates more faithful images better aligned with user intention in the presence of ambiguities. |
Controlled Data Generation via Insertion Operations for NLU (2022.naacl-industry)
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| Challenge: | a new approach to annotate live traffic is emerging to be cost-effective and efficient . manual data annotation is expensive and not preferred for meeting customer privacy expectations . |
| Approach: | They propose a targeted synthetic data generation technique by inserting tokens into a given semantic signature. |
| Outcome: | The proposed approach achieves the same accuracy as training with all available data on a voice assistant dataset. |
Improving Large-Scale Conversational Assistants using Model Interpretation based Training Sample Selection (2022.emnlp-industry)
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Stefan Schroedl, Manoj Kumar, Kiana Hajebi, Morteza Ziyadi, Sriram Venkatapathy, Anil Ramakrishna, Rahul Gupta, Pradeep Natarajan
| Challenge: | Large-scale, voice-based conversational assistants process each utterance through a multi-stage pipeline that includes wakeword detection, automatic speech recognition (ASR), natural language understanding (NLU), entity resolution, and textto-speech. |
| Approach: | They propose a method to identify customer implicitly satisfied with Alexa's responses by leveraging interpretations of model behavior. |
| Outcome: | The proposed approach produces statistically significant improvements in both offline and online tests. |
BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain (2024.naacl-long)
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| Challenge: | Existing models for accounting databases that can be queried using natural language are lacking in some domains. |
| Approach: | They propose a large-scale text-to-SQL dataset for accounting and financial domains . they propose 'bookSQl' to be used to query accounting databases using natural language . |
| Outcome: | The proposed model performs poorly on the existing model, pointing towards a more focused model for this domain. |
Chasing the Tail with Domain Generalization: A Case Study on Frequency-Enriched Datasets (2022.aacl-main)
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| Challenge: | In academic research, natural language understanding tasks are typically defined by creating annotated datasets in which each utterance is encountered once. |
| Approach: | They propose a method that explicitly uses utterance frequency in training data to learn models that are more robust to unknown distributions. |
| Outcome: | The proposed approach shows up to 7.02% relative improvement over baselines on the tail data. |