Papers by Rishabh Kumar
Adversarial Examples for Evaluating Math Word Problem Solvers (2021.findings-emnlp)
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| Challenge: | Existing MWP solvers do not understand language and its relation with numbers, and their accuracy is unclear. |
| Approach: | They propose two methods to generate adversarial attacks to evaluate the robustness of existing MWP solvers. |
| Outcome: | The proposed method reduces the accuracy of existing MWP solvers by over 40% on two benchmark datasets. |
Rolling Out Data Quality Overnight, without losing the plot: A Multi-Agent System for Speech Data Quality Management (2026.findings-acl)
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Rishabh Kumar, Abhinav Painuli, Chriss Philip Saji, Devesh Soni, Amrith Krishna, Ganesh Ramakrishnan
| Challenge: | Using automation to improve quality management is expensive and resource-intensive for speech datasets. |
| Approach: | They propose a natural language-driven agentic framework that compiles user requirements into dependency-aware DAG workflows over modular tools for audio, transcript, and metadata verification. |
| Outcome: | The proposed framework achieves 80-90% agreement with expert verification while requiring less than 20% of the cost and time of manual QC. |
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights (2021.findings-acl)
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| Challenge: | In this paper, we propose the first large scale study of automatic speech recognition in Sanskrit . we focus on the impact of unit selection in San's ASR systems . |
| Approach: | They propose a large scale study of automatic speech recognition in Sanskrit . they propose syllable level unit selection that captures character sequences . |
| Outcome: | The proposed model captures character sequences from one vowel in the word to the next vowela. |
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue (2020.lrec-1)
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| Challenge: | Current architectures only take care of semantic and contextual information for a given query and fail to fully account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. |
| Approach: | They propose a multi-stream deep learning architecture that learns unified embeddings for query-response pairs by incorporating Graph Convolution Networks over their dependency parse. |
| Outcome: | The proposed architecture improves on the next sentence prediction task and significantly improves existing techniques. |
Post-ASR Correction in Hindi: Comparing Language Models and Large Language Models in Low-Resource Scenarios (2026.eacl-short)
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| Challenge: | Automatic Speech Recognition (ASR) systems for low-resource languages produce erroneous transcripts due to limited annotated data and linguistic complexity. |
| Approach: | They compare language models and large language models for post-ASR correction in Hindi . they observe a scaling trend under zero-shot ICL where mid-sized LLMs degrade performance before marginal recovery at extreme scales. |
| Outcome: | The proposed model outperforms larger models in both fine-tuning and in-context learning settings. |
Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models (2024.findings-emnlp)
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| Challenge: | In this work, we address the challenge of cross-lingual proper noun recognition in automatic speech recognition systems where proper nodes in an utterance may originate from a language different from the language in which the ASR system is trained. |
| Approach: | They propose a dictionary-based method to correct ASR predictions in a large language model . |
| Outcome: | The proposed method significantly reduces word error rates across cross-lingual proper noun recognition tasks involving three secondary languages. |
Adding SPICE to Life: Speaker Profiling in Multiparty Conversations (2024.lrec-main)
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| Challenge: | Prior studies assumed the speaker’s persona’s immediate availability, a premise not universally applicable. |
| Approach: | They propose to synthesize persona attributes for each dialogue participant by combining three core tasks: persona discovery, persona-type identification, and persona value extraction. |
| Outcome: | The proposed task synthesizes persona attributes for each dialogue participant . the resulting model is compared against a baseline model and the proposed model is robust. |
Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers (2022.naacl-main)
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| Challenge: | Existing Math Word Problem solvers do not generalize well and rely on superficial cues to achieve high performance. |
| Approach: | They propose several data augmentation techniques to increase the size of existing MWP datasets by five folds by deploying them to a benchmark dataset. |
| Outcome: | The proposed methods increase the generalization and robustness of existing solvers by over five percentage points on benchmark datasets. |