Portrait of Min Ma is unavailable

Min Ma

Affiliate Member
Research Scientist, Google DeepMind
Research Topics
AGI (Artificial General Intelligence)
AI4Humanity
Generative Models
LLM Agent
Machine Learning For Speech and Audio
Reasoning
Google Scholar

Publications

VoxSumm: A Multilingual Corpus of Long-Form Spoken News for Joint Summarization and Translation
Yejin Jeon
Marie Maltais
Virginia Ceccatelli
Min Ma
David Ifeoluwa Adelani
As information increasingly traverses linguistic boundaries, users require concise cross-lingual representations of long-form content. Never… (see more)theless, long-document summarization research remains text-centric, whereas multilingual speech research has largely prioritized translation, preserving source content rather than compressing it. We address this methodological gap by formalizing joint speech summarization and translation (JSumT): the generation of a succinct, faithful target-language summary directly from a long spoken document in a source language. We additionally introduce VoxSumm, the first multilingual and cross-lingual benchmark for this task, comprising 10,045 BBC article-summary pairs across 24 languages and encompassing approximately 703 hours of speech data. Our evaluation of representative speech-language models reveals pronounced variation across models and generation settings: Gemini3.1-Pro demonstrates the greatest consistency, summarization into English generally surpasses generation into non-English target languages, and translating an entire document before summarization compounds instruction-following failures. Through the release of VoxSumm, we establish a foundation for developing and evaluating multilingual systems capable of jointly interpreting, compressing, and translating long-form speech.
2026-08-10
arXiv (preprint)
doi.org
arxiv.org
NaijaS2ST: A Multi-Accent Benchmark for Speech-to-Speech Translation in Low-Resource Nigerian Languages
Marie Maltais
Yejin Jeon
Min Ma
Shamsuddeen Hassan Muhammad
Idris Abdulmumin
Maryam Ibrahim Mukhtar
Daud Abolade
Joel Okepefi
Johnson Sewedo
David Ifeoluwa Adelani
Speech translation for low-resource languages remains fundamentally limited by the scarcity of high-quality, diverse parallel speech data, a… (see more) challenge that is especially pronounced in African linguistic contexts. To address this, we introduce NaijaS2ST, a parallel speech translation dataset spanning Igbo, Hausa, Yor\`ub\'a, and Nigerian Pidgin paired with English. The dataset comprises approximately 50 hours of speech per language and captures substantial variation in speakers and accents, reflecting realistic multilingual and multi-accent conditions. With NaijaS2ST, we conduct a comprehensive benchmark of cascaded, end-to-end (E2E), and AudioLLM-based approaches across bidirectional translation settings. Our results show that audio LLMs with few-shot examples are more effective for speech-to-text translation than cascaded and end-to-end methods trained on fine-tuned data. However, for speech-to-speech translation, the cascaded and audio LLM paradigms yield comparable performance, indicating that there is still considerable room for improvement in developing targeted, task-specific models for this setting. By providing both a high-quality dataset and a systematic benchmark, we hope that NaijaS2ST will serve as a strong foundation for advancing research in low-resource, multilingual speech translation.
2026-04-16
arXiv (preprint)
doi.org
arxiv.org
Reassessing Speech Translation for Low-Resource Languages: Do LLMs Redefine the State-of-the-Art Against Cascaded Models?
Jonah Dauvet
Min Ma
Jessica Ojo
David Ifeoluwa Adelani
2025-10-31
MRL @ International Conference on Learning Representations (published)
doi.org
mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks
Luel Hagos Beyene
Vivek Verma
Min Ma
Jesujoba Oluwadara Alabi
Fabian David Schmidt
Joyce Nakatumba-Nabende
David Ifeoluwa Adelani
Large Language models (LLMs) have demonstrated impressive performance on a wide range of tasks, including in multimodal settings such as spe… (see more)ech. However, their evaluation is often limited to English and a few high-resource languages. For low-resource languages, there is no standardized evaluation benchmark. In this paper, we address this gap by introducing mSTEB, a new benchmark to evaluate the performance of LLMs on a wide range of tasks covering language identification, text classification, question answering, and translation tasks on both speech and text modalities. We evaluated the performance of leading LLMs such as Gemini 2.0 Flash and GPT-4o (Audio) and state-of-the-art open models such as Qwen 2 Audio and Gemma 3 27B. Our evaluation shows a wide gap in performance between high-resource and low-resource languages, especially for languages spoken in Africa and Americas/Oceania. Our findings show that more investment is needed to address their under-representation in LLMs coverage.
2025-06-09
ArXiv (preprint)
doi.org
arxiv.org
Gemma 3 Technical Report
Gemma Team Aishwarya Kamath
Johan Ferret
Shreya Pathak
Nino Vieillard
Ramona Merhej
Sarah Perrin
Tatiana Matejovicova
Alexandre Ram'e
Morgane Rivière
Louis Rouillard
Thomas Mesnard
Geoffrey Cideron
Jean-Bastien Grill
Sabela Ramos
Edouard Yvinec
Michelle Casbon
Etienne Pot
Ivo Penchev
Gael Liu
Francesco Visin … (see 190 more)
Kathleen Kenealy
Lucas Beyer
Xiaohai Zhai
Anton Tsitsulin
Róbert Busa-Fekete
Alex Feng
Noveen Sachdeva
Benjamin Coleman
Yi Gao
Basil Mustafa
Iain Barr
Emilio Parisotto
David Tian
Matan Eyal
Colin Cherry
Jan-Thorsten Peter
Danila Sinopalnikov
Surya Bhupatiraju
Rishabh Agarwal
Mehran Kazemi
Dan Malkin
Ravin Kumar
David Vilar
Idan Brusilovsky
Jiaming Luo
Andreas Steiner
Abe Friesen
Abhanshu Sharma
Abheesht Sharma
Adi Mayrav Gilady
Adrian Goedeckemeyer
Alaa Saade
Alexander Kolesnikov
Alexei Bendebury
Alvin Abdagic
Amit Vadi
Andr'as Gyorgy
André Susano Pinto
Anil Das
Ankur Bapna
Antoine Miech
Antoine Yang
Antonia Paterson
Ashish Shenoy
Ayan Chakrabarti
Bilal Piot
Boxi Wu
Bobak Shahriari
Bryce Petrini
Charlie Chen
Charline Le Lan
Christopher A. Choquette-Choo
CJ Carey
Cormac Brick
Daniel Deutsch
Danielle Eisenbud
Dee Cattle
Derek Cheng
Dimitris Paparas
Divyashree Shivakumar Sreepathihalli
Doug Reid
Dustin Tran
Dustin Zelle
Eric Noland
Erwin Huizenga
Eugene Kharitonov
Frederick Liu
Gagik Amirkhanyan
Glenn Cameron
Hadi Hashemi
Hanna Klimczak-Pluci'nska
Harman Singh
Harsh Mehta
Harshal Tushar Lehri
Hussein Hazimeh
Ian Ballantyne
Idan Szpektor
Ivan Nardini
Jean Pouget-Abadie
Jetha Chan
Joe Stanton
J. Michael Wieting
Jonathan Lai
Jordi Orbay
Joe Fernandez
Joshua Newlan
Junsong Ji
Jyotinder Singh
Kat Black
Kathy Yu
Kevin Hui
Kiran N. Vodrahalli
Klaus Greff
Linhai Qiu
Marcella Valentine
Marina Coelho
Marvin Ritter
Matt Hoffman
Matthew Watson
Mayank Chaturvedi
Michael Moynihan
Min Ma
Nabila Babar
Natasha Noy
Nathan Byrd
Nick Roy
Nikola Momchev
Nilay Chauhan
Oskar Bunyan
Pankil Botarda
Paul Caron
Paul Kishan Rubenstein
Phil Culliton
Philipp Schmid
Pier Giuseppe Sessa
Pingmei Xu
Piotr Stańczyk
Pouya Dehghani Tafti
Rakesh Shivanna
Renjie Wu
Renke Pan
R. Rokni
Rob Willoughby
Rohith Vallu
Ryan Mullins
Sammy Jerome
Sara Smoot
Sertan Girgin
Shariq Iqbal
Shashir Reddy
Shruti Sheth
Siim Põder
Sijal Bhatnagar
S. Panyam
Sivan Eiger
Susan Zhang
Tianqi Liu
Trevor Yacovone
T. Liechty
Uday Kalra
Utku Evci
Vedant Misra
Vincent Roseberry
Vladimir Feinberg
Vlad Kolesnikov
Woohyun Han
Woosuk Kwon
X. T. Chen
Yinlam Chow
Yuvein Zhu
Zichuan Wei
Z. Egyed
Victor Cotruta
Minh Giang
Phoebe Kirk
Anand Rao
Jessica Lo
Erica Moreira
Luiz GUStavo Martins
Omar Sanseviero
Lucas Gonzalez
Zach Gleicher
Tris Brian Warkentin
Seyed Vahab Mirrokni
Evan Senter
Eli Collins
Joelle Barral
Zoubin Ghahramani
Raia Hadsell
Yossi Matias
D. Sculley
Slav Petrov
Noah Fiedel
Noam M. Shazeer
Oriol Vinyals
Jeffrey Dean
Demis Hassabis
Koray Kavukcuoglu
Clément Farabet
Elena Buchatskaya
Jean-Baptiste Alayrac
Rohan Anil
Dmitry Lepikhin
Sebastian Borgeaud
Olivier Bachem
Armand Joulin
Alek Andreev
Cassidy Hardin
Robert Dadashi
L'eonard Hussenot
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters… (see more). This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achieved by increasing the ratio of local to global attention layers, and keeping the span on local attention short. The Gemma 3 models are trained with distillation and achieve superior performance to Gemma 2 for both pre-trained and instruction finetuned versions. In particular, our novel post-training recipe significantly improves the math, chat, instruction-following and multilingual abilities, making Gemma3-4B-IT competitive with Gemma2-27B-IT and Gemma3-27B-IT comparable to Gemini-1.5-Pro across benchmarks. We release all our models to the community.
2025-03-24
ArXiv (preprint)
doi.org
arxiv.org
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder
Jonathan H. Clark
Alexander Gutkin
Mihir Kale
Min Ma
Massimo Nicosia
Shruti Rijhwani
Parker Riley
Jean Michel Amath Sarr
Xinyi Wang
John Frederick Wieting
Nitish Gupta
Anna Katanova
Christo Kirov
Dana L Dickinson
Brian Roark
Bidisha Samanta
Connie Tao
David Ifeoluwa Adelani
Vera Axelrod … (see 7 more)
Isaac Rayburn Caswell
Colin Cherry
Dan Garrette
Reeve Ingle
Melvin Johnson
Dmitry Panteleev
Partha Talukdar
Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- l… (see more)anguages for which NLP re-search is particularly far behind in meeting user needs -- it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks -- tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text-only, multi-modal (vision, audio, and text),supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models
2023-11-30
Findings of the Association for Computational Linguistics: EMNLP 2023 (published)
doi.org
openreview.net