Quyet V. Do — accepted AI-conference papers

2024

SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages

Holy Lovenia, Rahmad Mahendra, Salsabil Maulana Akbar, Lester James Validad Miranda, Jennifer Santoso, Elyanah Aco +55

EMNLP 2024main

Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA, compromising…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2023

COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective

Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang +3

ACL 2023long

Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, exploiting context plays an essential role in detecting causal relations. Meanwhile,…

Cited by 17 Source ↗PDF ↗code ↗Scholar ↗

2022

PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population

Tianqing Fang, Quyet V. Do, Hongming Zhang, Yangqiu Song, Ginny Y. Wong, Simon See

EMNLP 2022finding

Commonsense Knowledge Base (CSKB) Population aims at reasoning over unseen entities and assertions on CSKBs, and is an important yet hard commonsense reasoning task. One challenge is that it requires out-of-domain generalization ability as the source CSKB for training is of a relatively smaller scal…

Cited by 8 Source ↗PDF ↗code ↗Scholar ↗