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Thanmay Jayakumar

5 accepted papers

2025

CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation

Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan, Kareem Elzeky, Henok Biadglign Ademtew, Alham Fikri Aji +29
EMNLP 2025

Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural

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2025

RomanLens: The Role Of Latent Romanization In Multilinguality In LLMs

Alan Saji, Jaavid Aktar Husain, Thanmay Jayakumar, Raj Dabre, Anoop Kunchukuttan, Ratish Puduppully
ACL 2025finding

Large Language Models (LLMs) exhibit strong multilingual performance despite being predominantly trained on English-centric corpora. This raises a fundamental question: How do LLMs achieve such multilingual capabilities? Focusing on languages written in non-Roman scripts, we investigate the role of…

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2024

CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark

David Orlando Romero Mogrovejo, Chenyang Lyu, Haryo Akbarianto Wibowo, Santiago Góngora, Aishik Mandal, Sukannya Purkayastha +70
NeurIPS 2024oral

Visual Question Answering~(VQA) is an important task in multimodal AI, which requires models to understand and reason on knowledge present in visual and textual data. However, most of the current VQA datasets and models are primarily focused on English and a few major world languages, with images th…

Cited by 34Source ↗PDF ↗Scholar ↗
2024

Leveraging Linguistically Enhanced Embeddings for Open Information Extraction

Fauzan Nayeem Farooqui, Thanmay Jayakumar, Pulkit Mathur, Mansi A. Radke
COLING 2024main

Open Information Extraction (OIE) is a structure prediction (SP) task in Natural Language Processing (NLP) that aims to extract structured n-ary tuples - usually subject-relation-object triples - from free text. The word embeddings in the input text can be enhanced with linguistic features, usually…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization

Jaavid J, Raj Dabre, Aswanth M, Jay Gala, Thanmay Jayakumar, Ratish Puduppully +1
ACL 2024long

This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages, specifically those using non-Roman scripts. We propose an approach that utilizes the romanized form of text as an interface for LLMs, hypothesizing that its frequent informal use and shared tokens…

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