Papers by Go Inoue
Do Diacritics Matter? Evaluating the Impact of Arabic Diacritics on Tokenization and LLM Benchmarks (2026.findings-eacl)
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| Challenge: | Diacritics can significantly influence language processing tasks in Arabic . their presence can increase subword fragmentation during tokenization, reducing performance . |
| Approach: | They analyze the impact of diacritics on tokenization and benchmark task performance across major Large Language Models. |
| Outcome: | The proposed model is robust to diacritics, but full diacritization leads to token fragmentation and degraded performance. |
Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects (2022.findings-acl)
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| Challenge: | Pre-trained morphosyntactic tagging models outperform existing systems in Modern Standard Arabic and all the Arabic dialects studied. |
| Approach: | They present results on morphosyntactic tagging across different varieties of Arabic using pre-trained transformer language models. |
| Outcome: | The proposed models outperform existing systems in Modern Standard Arabic, 2.8% in Gulf, 1.6% in Egyptian, and 8.3% in Levantine. |
A Parallel Corpus of Arabic-Japanese News Articles (L18-1)
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| Challenge: | a large-scale parallel corpora with manually verified subsets of sentences has been used for machine translation between major language pairs. |
| Approach: | They describe the creation process and statistics of the Arabic-Japanese portion of the TUFS Media Corpus . they also report the first results of Arabic-japanese phrase-based machine translation trained on the corpus based on the Arabic corpus. |
| Outcome: | The proposed corpus is a document-level parallel corpus and sentence-level parser corpus . it is the first time that Arabic-Japanese translations have been trained on it . |
CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese (2024.lrec-main)
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| Challenge: | Despite numerous efforts in ad text generation, the aspect of diversifying a text has received limited attention, particularly in non-English languages like Japanese. |
| Approach: | They present a dataset for ad text generation in Japanese using annotators to examine the capabilities of recent NLG models. |
| Outcome: | The proposed dataset includes 3,980 ad texts written by experts taking into account various aspects of ade appeals. |
The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic (2022.lrec-1)
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| Challenge: | Various corpora of various sizes and representing different genres, have been created for various Arabic dialects. |
| Approach: | They propose to create a specialized corpus of Bahraini Arabic dialect, which includes written texts as well as transcripts of audio files. |
| Outcome: | The proposed corpus includes 620K words representing the Bahraini Arabic dialect . the annotated corpus is available to support researchers interested in Arabic NLP . |
Camelira: An Arabic Multi-Dialect Morphological Disambiguator (2022.emnlp-demos)
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| Challenge: | Camelira is a web-based Arabic multi-dialect morphological disambiguation tool that covers modern standard Arabic, Egyptian, Gulf, and Levantine. |
| Approach: | They propose a web-based Arabic multi-dialect morphological disambiguation tool that covers modern standard Arabic, Egyptian, Gulf, and Levantine. |
| Outcome: | The proposed tool covers modern standard Arabic, Egyptian, Gulf, and Levantine . it also provides an option to automatically choose an appropriate disambiguator based on the prediction of a dialect identification component. |
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing (2020.lrec-1)
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Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, Nizar Habash
| Challenge: | CAMeL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Approach: | They present CAMeL Tools, an open-source Python toolkit for Arabic natural language processing . CAMeleL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Outcome: | The proposed tools are based on CAMeL Tools, an open-source Python toolkit for Arabic natural language processing. |
EMAD: A Bridge Tagset for Unifying Arabic POS Annotations (2024.lrec-main)
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| Challenge: | Existing tagsets for Arabic are difficult to combine due to the diversity of their features. |
| Approach: | They propose an Arabic Extended Morphological Analysis and Disambiguation Tagset which facilitates conversion and unification of Arabic tagsets. |
| Outcome: | The proposed tagset facilitates conversion and unification of different tagsetes used to annotate Arabic corpora. |
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation (2023.emnlp-main)
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| Challenge: | Existing studies on grammatical error correction (GEC) in morphologically rich languages have been limited due to data scarcity and language complexity. |
| Approach: | They propose to use Arabic GEC to improve performance across three datasets . they define Arabic grammatical error detection task as auxiliary input . |
| Outcome: | The proposed models achieve SOTA results on two Arabic GEC shared task datasets and establish a strong benchmark on a recently created dataset. |
A Culturally-diverse Multilingual Multimodal Video Benchmark & Model (2025.emnlp-main)
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Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz, Hanoona Abdul Rasheed, Dinura Dissanayake, Mohammed Irfan Kurpath, Yahya Hmaiti, Go Inoue, Jean Lahoud, Md. Safirur Rashid, Shadid Intisar Quasem, Maheen Fatima, Franco Vidal, Mykola Maslych, Ketan Pravin More, Sanoojan Baliah, Hasindri Watawana, Yuhao Li, Fabian Farestam, Leon Schaller, Roman Tymtsiv, Simon Weber, Hisham Cholakkal, Ivan Laptev, Shin’ichi Satoh, Michael Felsberg, Mubarak Shah, Salman Khan, Fahad Shahbaz Khan
| Challenge: | Large multimodal models have gained attention for their effectiveness to understand and generate descriptions of visual content. |
| Approach: | They propose a multilingual Video LMM benchmark to evaluate video LMMs across 14 languages . they also introduce a machine translated multilingual video training set . |
| Outcome: | The proposed video LMM benchmark is designed to evaluate video Lmms across 14 languages including Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. |