Papers by Haryo Wibowo
COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances (2024.naacl-long)
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| Challenge: | Existing multilingual language models struggle to capture local nuances and contexts that vary from culture to culture. |
| Approach: | They propose a public Indonesian language common sense reasoning dataset COPAL-ID . it incorporates Indonesian local and cultural nuances and provides a more natural portrayal of causal reasoning . |
| Outcome: | The proposed dataset is fluent and free from awkward phrases, unlike the previous dataset. |
NusaCrowd: Open Source Initiative for Indonesian NLP Resources (2023.findings-acl)
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Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji, Genta Winata, Bryan Wilie, Fajri Koto, Rahmad Mahendra, Christian Wibisono, Ade Romadhony, Karissa Vincentio, Jennifer Santoso, David Moeljadi, Cahya Wirawan, Frederikus Hudi, Muhammad Satrio Wicaksono, Ivan Parmonangan, Ika Alfina, Ilham Firdausi Putra, Samsul Rahmadani, Yulianti Oenang, Ali Septiandri, James Jaya, Kaustubh Dhole, Arie Suryani, Rifki Afina Putri, Dan Su, Keith Stevens, Made Nindyatama Nityasya, Muhammad Adilazuarda, Ryan Hadiwijaya, Ryandito Diandaru, Tiezheng Yu, Vito Ghifari, Wenliang Dai, Yan Xu, Dyah Damapuspita, Haryo Wibowo, Cuk Tho, Ichwanul Karo Karo, Tirana Fatyanosa, Ziwei Ji, Graham Neubig, Timothy Baldwin, Sebastian Ruder, Pascale Fung, Herry Sujaini, Sakriani Sakti, Ayu Purwarianti
| Challenge: | Existing NLP research in Indonesian languages has been held back by factors such as language diversity, orthographic variation, resource limitation and other societal challenges. |
| Approach: | They present a collaborative initiative to collect and unify existing resources for Indonesian languages and open access to previously non-public resources. |
| Outcome: | The results show that the datasets are highly reliable and can be used to generate the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. |
On “Scientific Debt” in NLP: A Case for More Rigour in Language Model Pre-Training Research (2023.acl-long)
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Made Nindyatama Nityasya, Haryo Wibowo, Alham Fikri Aji, Genta Winata, Radityo Eko Prasojo, Phil Blunsom, Adhiguna Kuncoro
| Challenge: | Despite rapid recent progress, current research practices conflate different sources of model improvement without conducting proper ablation studies and principled comparisons . authors conclude with recommendations for how to encourage and incentivize this line of work . |
| Approach: | They critique current research practices in the field of language model pre-training . they examine the success of language models pre-trained on large amounts of data . |
| Outcome: | The proposed models can achieve competitive or better performance than BERT under comparable conditions. |