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Anirudh Srinivasan

4 accepted papers

2026

M3Grounder: Mask-Based Multi-Span and Multi-Granular Grounding for Document QA

CVPR 2026

**Document QA** requires not only accurate answers but also identifying where each answer is grounded on the page. Most models treat the task as text-only generation, while existing answer grounding methods generate coarse bounding boxes that fail to capture curved text. We introduce **M3Grounder, a

Cited by 0SourceScholar
2025

To Answer or Not to Answer (TAONA): A Robust Textual Graph Understanding and Question Answering Approach

EMNLP 2025

Recently, textual graph-based retrieval-augmented generation (GraphRAG) has gained popularity for addressing hallucinations in large language models when answering domain-specific questions. Most existing studies assume that generated answers should comprehensively integrate all relevant information

Cited by 0SourcePDFScholar
2024

Textless Speech-to-Speech Translation With Limited Parallel Data

EMNLP 2024finding

Existing speech-to-speech translation (S2ST) models fall into two camps: they either leverage text as an intermediate step or require hundreds of hours of parallel speech data. Both approaches are incompatible with textless languages or language pairs with limited parallel data. We present PFB, a fr…

2022

TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages

EMNLP 2022finding

We study politeness phenomena in nine typologically diverse languages. Politeness is an important facet of communication and is sometimes argued to be cultural-specific, yet existing computational linguistic study is limited to English. We create TyDiP, a dataset containing three-way politeness anno…