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Go Inoue

6 accepted papers

2025

A Culturally-diverse Multilingual Multimodal Video Benchmark & Model

Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz, Hanoona Abdul Rasheed, Dinura Dissanayake, Mohammed Irfan Kurpath +23
EMNLP 2025

Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in English language. While few recent works explore multilingual image LMMs, to the best of our knowledge, moving beyond the Engl

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2024

Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization

Salman Elgamal, Ossama Obeid, Mhd Kabbani, Go Inoue, Nizar Habash
ACL 2024long

The widespread absence of diacritical marks in Arabic text poses a significant challenge for Arabic natural language processing (NLP). This paper explores instances of naturally occurring diacritics, referred to as “diacritics in the wild,” to unveil patterns and latent information across six divers…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2024

CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese

Go Inoue, Akihiko Kato, Masato Mita, Ukyo Honda, Peinan Zhang
COLING 2024main

Ad text generation is the task of creating compelling text from an advertising asset that describes products or services, such as a landing page. In advertising, diversity plays an important role in enhancing the effectiveness of an ad text, mitigating a phenomenon called “ad fatigue,” where users b…

Cited by 2Source ↗PDF ↗Scholar ↗
2023

Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation

Bashar Alhafni, Go Inoue, Christian Khairallah, Nizar Habash
EMNLP 2023long main

Grammatical error correction (GEC) is a well-explored problem in English with many existing models and datasets. However, research on GEC in morphologically rich languages has been limited due to challenges such as data scarcity and language complexity. In this paper, we present the first results on…

Cited by 0Source ↗code ↗Scholar ↗
2022

Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects

Go Inoue, Salam Khalifa, Nizar Habash
ACL 2022findings

We present state-of-the-art results on morphosyntactic tagging across different varieties of Arabic using fine-tuned pre-trained transformer language models. Our models consistently outperform existing systems in Modern Standard Arabic and all the Arabic dialects we study, achieving 2.6% absolute im…

Cited by 25Source ↗PDF ↗code ↗Scholar ↗