10 citations · 31 across the 16 of their papers we have counts for
20 papers
Beyond Translation: Cross-Cultural Meme Transcreation with Vision-Language Models
Yuming Zhao, Peiyi Zhang, Oana Ignat
Memes are a pervasive form of online communication, yet their cultural specificity poses significant challenges for cross-cultural adaptation. We study cross-cultural meme transcre…
Beyond Factual QA: Mentorship-Oriented Question Answering over Long-Form Multilingual Content
Parth Bhalerao, Ruiwen Guan, Diola Dsouza +1
Question answering systems are typically evaluated on factual correctness, yet many real-world applications-such as education and career guidance-require mentorship: responses that…
Culture Affordance Atlas: Reconciling Object Diversity Through Functional Mapping
Joan Nwatu, Longju Bai, Oana Ignat +1
Culture shapes the objects people use and for what purposes, yet mainstream Vision-Language (VL) datasets frequently exhibit cultural biases, disproportionately favoring higher-inc…
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment
Antonia Karamolegkou, Angana Borah, Eunjung Cho +30
Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social G…
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin +18
We present our shared task on text-based emotion detection, covering more than 30 languages from seven distinct language families. These languages are predominantly low-resource an…
When Cultures Meet: Multicultural Text-to-Image Generation
Parth Bhalerao, Mounika Yalamarty, Brian Trinh +1
Text-to-image generation models have achieved strong performance in culturally homogeneous settings, yet their ability to generate multicultural scenes, where people and landmarks…
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin +45
People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition--an umbrella term for several NLP tasks--impacts various applications wit…
The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning
Longju Bai, Angana Borah, Oana Ignat +1
Large Multimodal Models (LMMs) exhibit impressive performance across various multimodal tasks. However, their effectiveness in cross-cultural contexts remains limited due to the pr…
Why AI Is WEIRD and Should Not Be This Way: Towards AI For Everyone, With Everyone, By Everyone
Rada Mihalcea, Oana Ignat, Longju Bai +7
This paper presents a vision for creating AI systems that are inclusive at every stage of development, from data collection to model design and evaluation. We address key limitatio…
Uplifting Lower-Income Data: Strategies for Socioeconomic Perspective Shifts in Large Multi-modal Models
Joan Nwatu, Oana Ignat, Rada Mihalcea
Recent work has demonstrated that the unequal representation of cultures and socioeconomic groups in training data leads to biased Large Multi-modal (LMM) models. To improve LMM mo…
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge pres…
MAiDE-up: Multilingual Deception Detection of GPT-generated Hotel Reviews
Oana Ignat, Xiaomeng Xu, Rada Mihalcea
Deceptive reviews are becoming increasingly common, especially given the increase in performance and the prevalence of LLMs. While work to date has addressed the development of mod…
Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Data
Oana Ignat, Gayathri Ganesh Lakshmy, Rada Mihalcea
Inspiration is linked to various positive outcomes, such as increased creativity, productivity, and happiness. Although inspiration has great potential, there has been limited effo…
Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data
Shinka Mori, Oana Ignat, Andrew Lee +1
Synthetic data generation has the potential to impact applications and domains with scarce data. However, before such data is used for sensitive tasks such as mental health, we nee…
Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models
Joan Nwatu, Oana Ignat, Rada Mihalcea
Despite the impressive performance of current AI models reported across various tasks, performance reports often do not include evaluations of how these models perform on the speci…
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models
Oana Ignat, Zhijing Jin, Artem Abzaliev +19
Recent progress in large language models (LLMs) has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse t…
OCR Improves Machine Translation for Low-Resource Languages
Oana Ignat, Jean Maillard, Vishrav Chaudhary +1
We aim to investigate the performance of current OCR systems on low resource languages and low resource scripts. We introduce and make publicly available a novel benchmark, OCR4MT,…
WhyAct: Identifying Action Reasons in Lifestyle Vlogs
Oana Ignat, Santiago Castro, Hanwen Miao +2
We aim to automatically identify human action reasons in online videos. We focus on the widespread genre of lifestyle vlogs, in which people perform actions while verbally describi…
FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework
Santiago Castro, Ruoyao Wang, Pingxuan Huang +5
We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER -- a novel dataset consisting of 28,000 videos and descriptions in support of this e…
Identifying Visible Actions in Lifestyle Vlogs
Oana Ignat, Laura Burdick, Jia Deng +1
We consider the task of identifying human actions visible in online videos. We focus on the widely spread genre of lifestyle vlogs, which consist of videos of people performing act…