Radityo Eko Prasojo


2023

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On “Scientific Debt” in NLP: A Case for More Rigour in Language Model Pre-Training Research
Made Nindyatama Nityasya | Haryo Wibowo | Alham Fikri Aji | Genta Winata | Radityo Eko Prasojo | Phil Blunsom | Adhiguna Kuncoro
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This evidence-based position paper critiques current research practices within the language model pre-training literature. Despite rapid recent progress afforded by increasingly better pre-trained language models (PLMs), current PLM research practices often conflate different possible sources of model improvement, without conducting proper ablation studies and principled comparisons between different models under comparable conditions. These practices (i) leave us ill-equipped to understand which pre-training approaches should be used under what circumstances; (ii) impede reproducibility and credit assignment; and (iii) render it difficult to understand: “How exactly does each factor contribute to the progress that we have today?” We provide a case in point by revisiting the success of BERT over its baselines, ELMo and GPT-1, and demonstrate how — under comparable conditions where the baselines are tuned to a similar extent — these baselines (and even-simpler variants thereof) can, in fact, achieve competitive or better performance than BERT. These findings demonstrate how disentangling different factors of model improvements can lead to valuable new insights. We conclude with recommendations for how to encourage and incentivize this line of work, and accelerate progress towards a better and more systematic understanding of what factors drive the progress of our foundation models today.

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NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages
Genta Indra Winata | Alham Fikri Aji | Samuel Cahyawijaya | Rahmad Mahendra | Fajri Koto | Ade Romadhony | Kemal Kurniawan | David Moeljadi | Radityo Eko Prasojo | Pascale Fung | Timothy Baldwin | Jey Han Lau | Rico Sennrich | Sebastian Ruder
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Natural language processing (NLP) has a significant impact on society via technologies such as machine translation and search engines. Despite its success, NLP technology is only widely available for high-resource languages such as English and Chinese, while it remains inaccessible to many languages due to the unavailability of data resources and benchmarks. In this work, we focus on developing resources for languages in Indonesia. Despite being the second most linguistically diverse country, most languages in Indonesia are categorized as endangered and some are even extinct. We develop the first-ever parallel resource for 10 low-resource languages in Indonesia. Our resource includes sentiment and machine translation datasets, and bilingual lexicons. We provide extensive analyses and describe challenges for creating such resources. We hope this work can spark NLP research on Indonesian and other underrepresented languages.

2022

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One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia
Alham Fikri Aji | Genta Indra Winata | Fajri Koto | Samuel Cahyawijaya | Ade Romadhony | Rahmad Mahendra | Kemal Kurniawan | David Moeljadi | Radityo Eko Prasojo | Timothy Baldwin | Jey Han Lau | Sebastian Ruder
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

NLP research is impeded by a lack of resources and awareness of the challenges presented by underrepresented languages and dialects. Focusing on the languages spoken in Indonesia, the second most linguistically diverse and the fourth most populous nation of the world, we provide an overview of the current state of NLP research for Indonesia’s 700+ languages. We highlight challenges in Indonesian NLP and how these affect the performance of current NLP systems. Finally, we provide general recommendations to help develop NLP technology not only for languages of Indonesia but also other underrepresented languages.

2021

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IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words
Haryo Akbarianto Wibowo | Made Nindyatama Nityasya | Afra Feyza Akyürek | Suci Fitriany | Alham Fikri Aji | Radityo Eko Prasojo | Derry Tanti Wijaya
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter
Alham Fikri Aji | Made Nindyatama Nityasya | Haryo Akbarianto Wibowo | Radityo Eko Prasojo | Tirana Fatyanosa
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This paper describes our team’s submission for the Social Media Mining for Health (SMM4H) 2021 shared task. We participated in three subtasks: Classifying adverse drug effect, COVID-19 self-report, and COVID-19 symptoms. Our system is based on BERT model pre-trained on the domain-specific text. In addition, we perform data cleaning and augmentation, as well as hyperparameter optimization and model ensemble to further boost the BERT performance. We achieved the first rank in both classifying adverse drug effects and COVID-19 self-report tasks.

2020

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Benchmarking Multidomain English-Indonesian Machine Translation
Tri Wahyu Guntara | Alham Fikri Aji | Radityo Eko Prasojo
Proceedings of the 13th Workshop on Building and Using Comparable Corpora

In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language, and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic. In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and conversation, to train and benchmark some variants of transformer-based NMT models across the domains. We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models, and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data