Papers by Derry Tanti Wijaya
Learning Translations via Images with a Massively Multilingual Image Dataset (P18-1)
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| Challenge: | Existing datasets for learning translations of words are limited to a few high-resource languages and unrealistically easy settings. |
| Approach: | They propose a large-scale multilingual corpus of images labeled with the word they represent to facilitate translation research. |
| Outcome: | The proposed method improves on an unsupervised technique that has been limited to a few languages and unrealistic settings. |
Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show (2022.lrec-1)
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| Challenge: | Prior work identified and summarized scenes associated with a TV show by selecting a few representative social media posts (5 posts) that were published during the timeline of the scenes. |
| Approach: | They propose a method to predict social media posts associated with a TV show from those that are not-indicative. |
| Outcome: | The proposed method can predict posts indicative of what happened in a scene from those that are not-indicative based on high AUC's on social media posts associated with a popular TV show . |
IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words (2021.findings-acl)
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Haryo Akbarianto Wibowo, Made Nindyatama Nityasya, Afra Feyza Akyürek, Suci Fitriany, Alham Fikri Aji, Radityo Eko Prasojo, Derry Tanti Wijaya
| Challenge: | Existing research on word normalization in Indonesian language relies on static dictionaries and machine translation. |
| Approach: | They propose to use Twitter to annotate Indonesian colloquial words with their standard forms and their word formation types/tags to perform morphological word normalization. |
| Outcome: | The proposed dataset analyzes morphological word normalization on Indonesian colloquial Lexicons and provides a baseline for future work. |
Cultural and Geographical Influences on Image Translatability of Words across Languages (2021.naacl-main)
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| Challenge: | Neural machine translation models produce poor translations when there are few/no parallel sentences to train the models. |
| Approach: | They define image translatability as the translability of words as images associated with words in different languages that have a high degree of visual similarity. |
| Outcome: | The proposed model improves upon text-only models only marginally. |
BU-NEmo: an Affective Dataset of Gun Violence News (2022.lrec-1)
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Carley Reardon, Sejin Paik, Ge Gao, Meet Parekh, Yanling Zhao, Lei Guo, Margrit Betke, Derry Tanti Wijaya
| Challenge: | Using a dataset that contains headline and image pairings from 840 news articles, we explore the relationship between image and text influence on human emotional response. |
| Approach: | They propose to use a U.S. gun violence news dataset that contains headline and image pairings from 840 news articles with 15K high-quality crowdsourced annotations on emotional responses. |
| Outcome: | The proposed dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. |
A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information (2025.findings-acl)
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Lucky Susanto, Musa Izzanardi Wijanarko, Prasetia Anugrah Pratama, Zilu Tang, Fariz Akyas, Traci Hong, Ika Karlina Idris, Alham Fikri Aji, Derry Tanti Wijaya
| Challenge: | Prior research has focused on toxicity and polarization as separate problems . extreme polarizing deepens divisions, often leading to hostility and fragmentation . |
| Approach: | They propose to use a multi-label Indonesian dataset annotated for toxicity, polarization, and annotator demographic information to study polarizing language and toxicity. |
| Outcome: | The proposed dataset shows that polarization cues improve toxicity classification and vice versa. |
“Wikily” Supervised Neural Translation Tailored to Cross-Lingual Tasks (2021.emnlp-main)
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| Challenge: | Unsupervised neural machine translation models perform well in low-resource or distant languages. |
| Approach: | They propose a model that leverages Wikipedia for machine translation and cross-lingual tasks without supervision from external parallel data or supervised models in target language. |
| Outcome: | The proposed model outperforms supervised models in Arabic and English translation tasks. |
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines (2025.naacl-long)
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Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan, David Anugraha, Rifki Afina Putri, Wang Yutong, Adam Nohejl, Ubaidillah Ariq Prathama, Nedjma Ousidhoum, Afifa Amriani, Anar Rzayev, Anirban Das, Ashmari Pramodya, Aulia Adila, Bryan Wilie, Candy Olivia Mawalim, Cheng Ching Lam, Daud Abolade, Emmanuele Chersoni, Enrico Santus, Fariz Ikhwantri, Garry Kuwanto, Hanyang Zhao, Haryo Akbarianto Wibowo, Holy Lovenia, Jan Christian Blaise Cruz, Jan Wira Gotama Putra, Junho Myung, Lucky Susanto, Maria Angelica Riera Machin, Marina Zhukova, Michael Anugraha, Muhammad Farid Adilazuarda, Natasha Christabelle Santosa, Peerat Limkonchotiwat, Raj Dabre, Rio Alexander Audino, Samuel Cahyawijaya, Shi-Xiong Zhang, Stephanie Yulia Salim, Yi Zhou, Yinxuan Gui, David Ifeoluwa Adelani, En-Shiun Annie Lee, Shogo Okada, Ayu Purwarianti, Alham Fikri Aji, Taro Watanabe, Derry Tanti Wijaya, Alice Oh, Chong-Wah Ngo
| Challenge: | Vision Language Models struggle with cultural-specific knowledge, especially in languages other than English and in underrepresented cultural contexts. |
| Approach: | They propose a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects and a training dataset. |
| Outcome: | The proposed model performs better with correct location context, but struggles with adversarial contexts and predicting specific regional cuisines and languages. |
Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation (2024.lrec-main)
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Ge Gao, Jongin Kim, Sejin Paik, Ekaterina Novozhilova, Yi Liu, Sarah T. Bonna, Margrit Betke, Derry Tanti Wijaya
| Challenge: | Existing methods for classifying discrete emotions from news headlines have been limited to using headlines. |
| Approach: | They propose to use people’s free-text explanations to classify emotions elicited by news headlines to generate emotion explanations from headlines. |
| Outcome: | The proposed method improves on methods that only use headlines and train a pretrained model for explanation generation. |
Do Language Models Understand Honorific Systems in Javanese? (2025.acl-long)
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Mohammad Rifqi Farhansyah, Iwan Darmawan, Adryan Kusumawardhana, Genta Indra Winata, Alham Fikri Aji, Derry Tanti Wijaya
| Challenge: | Despite its cultural and linguistic significance, there has been limited progress in developing a comprehensive corpus to capture these variations for natural language processing (NLP) tasks. |
| Approach: | They propose to use a dataset to capture the nuances of Unggah-Ungguh Basa, the Javanese speech etiquette framework, to assess the ability of language models to process various levels of Javanesi honorifics. |
| Outcome: | The proposed dataset encapsulates the nuances of Unggah-Ungguh Basa, the Javanese speech etiquette framework. |
What Do Indonesians Really Need from Language Technology? A Nationwide Survey (2025.emnlp-main)
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| Challenge: | Despite efforts to develop NLP for Indonesia’s 700+ local languages, progress remains costly due to the need for direct engagement with native speakers. |
| Approach: | They conduct a nationwide survey to assess the actual needs of native Indonesian speakers. |
| Outcome: | The findings indicate that addressing language barriers is the most critical priority . concerns around privacy, bias, and the use of public data highlight the need for greater transparency and clear communication to support broader AI adoption. |
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data (2021.emnlp-demo)
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Vibhu Bhatia, Vidya Prasad Akavoor, Sejin Paik, Lei Guo, Mona Jalal, Alyssa Smith, David Assefa Tofu, Edward Edberg Halim, Yimeng Sun, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
| Challenge: | Existing frameworks for analyzing frames in multilingual text documents are available online and via an API. |
| Approach: | They propose a web-based system for analyzing frames in multilingual text documents . framework combines unsupervised and supervised machine learning and leverages a state-of-the-art multilingual language model . |
| Outcome: | The proposed framework can significantly improve frame prediction performance while requiring a small sample of manual annotations. |
Mitigating Translationese in Low-resource Languages: The Storyboard Approach (2024.lrec-main)
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Garry Kuwanto, Eno-Abasi E. Urua, Priscilla Amondi Amuok, Shamsuddeen Hassan Muhammad, Anuoluwapo Aremu, Verrah Otiende, Loice Emma Nanyanga, Teresiah W. Nyoike, Aniefon D. Akpan, Nsima Ab Udouboh, Idongesit Udeme Archibong, Idara Effiong Moses, Ifeoluwatayo A. Ige, Benjamin Ajibade, Olumide Benjamin Awokoya, Idris Abdulmumin, Saminu Mohammad Aliyu, Ruqayya Nasir Iro, Ibrahim Said Ahmad, Deontae Smith, Praise-EL Michaels, David Ifeoluwa Adelani, Derry Tanti Wijaya, Anietie Andy
| Challenge: | Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which introduce the translationese effect. |
| Approach: | They propose a method that uses storyboards to elicit more fluent and natural sentences from native speakers without direct exposure to the source text. |
| Outcome: | The proposed method compared with traditional translation-based methods in terms of accuracy and fluency. |
Multi-Label and Multilingual News Framing Analysis (2020.acl-main)
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| Challenge: | Recent studies have focused on news framing in English, but few studies have explored how it can be extended to other languages and in multi-label settings. |
| Approach: | They propose a method that leverages dictionary and few annotations to detect frames from just the headline in a low-resource context. |
| Outcome: | The proposed method performs better than translating the entire headline to the source language . it can be scaled up to many languages, even those without existing translation technologies . |
NusaAksara: A Multimodal and Multilingual Benchmark for Preserving Indonesian Indigenous Scripts (2025.acl-long)
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Muhammad Farid Adilazuarda, Musa Izzanardi Wijanarko, Lucky Susanto, Khumaisa Nur’aini, Derry Tanti Wijaya, Alham Fikri Aji
| Challenge: | NusaAksara covers 8 scripts across 7 languages, including low-resource languages not commonly seen in NLP benchmarks. |
| Approach: | They propose a benchmark for Indonesian scripts that includes their original scripts and a dataset that includes 8 scripts across 7 languages. |
| Outcome: | The proposed benchmark covers 8 scripts across 7 languages, including low-resource languages not commonly seen in NLP benchmarks. |
AugCSE: Contrastive Sentence Embedding with Diverse Augmentations (2022.aacl-main)
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| Challenge: | Similar work has shown that a single augmentation can be used to learn a robust generalpurpose representation with contrastive learning. |
| Approach: | They propose a unified framework to utilize diverse sets of data augmentations to achieve a better, general-purpose sentence embedding model. |
| Outcome: | The proposed framework achieves state-of-the-art results on downstream transfer tasks and performs competitively on semantic textual similarity tasks, using only unsupervised data. |
Prediction of People’s Emotional Response towards Multi-modal News (2022.aacl-main)
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Ge Gao, Sejin Paik, Carley Reardon, Yanling Zhao, Lei Guo, Prakash Ishwar, Margrit Betke, Derry Tanti Wijaya
| Challenge: | BU-NEmo dataset extends from 320 to 1,297 news headline and lead image pairings and collects 38,910 annotations in a crowdsourcing experiment. |
| Approach: | They extend the U.S. gun violence news-to-emotions dataset from 320 to 1,297 news headline and lead image pairings and collect annotations in a crowdsourcing experiment. |
| Outcome: | The proposed models outperform baseline models on the NEmo+ dataset by large margins across several metrics. |
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage (2021.findings-emnlp)
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Isidora Tourni, Lei Guo, Taufiq Husada Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
| Challenge: | Journalists have been using both text and images to frame news stories . lead images may carry additional background knowledge about the event . |
| Approach: | They find that combining lead images and contextual information with text improves news framing . they release the first multimodal news framming dataset related to gun violence in the u.s. |
| Outcome: | The study shows that combining lead images with text improves prediction of news frames . it also shows that using multiple modes of information improves frame image relevance . |
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs (2023.acl-long)
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| Challenge: | Despite their success, even the largest language models make mistakes. |
| Approach: | They propose a framework where one language model can generate critiques to improve its peer's performance. |
| Outcome: | The proposed framework improves the performance of a fixed model 200 times its size by 10% over other models. |