Papers with English training data
Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search (2023.acl-demo)
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| Challenge: | Using only English training data, ISI-Clear makes global events available on-demand in 100 languages . Using a fixed task, events may still shift from day to day . |
| Approach: | They propose a cross-lingual zero-shot event extraction system that makes global events available on-demand in 100 languages. |
| Outcome: | The proposed system can extract events from non-English documents in 100 languages. |
Towards Cross-Lingual Explanation of Artwork in Large-scale Vision Language Models (2025.findings-naacl)
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| Challenge: | LVLMs are increasingly capable of responding in multiple languages . however, there is a lack of evaluation tools for LVLs that handle multiple languages. |
| Approach: | They used an extended dataset in multiple languages to evaluate LVLMs' ability to generate explanations in multiple language combinations. |
| Outcome: | The proposed dataset in multiple languages evaluates LVLMs' ability to generate explanations in other languages. |
Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)
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| Challenge: | Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling . |
| Approach: | They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures . |
| Outcome: | The proposed framework performs comparable to state-of-the-art models on three languages without annotations. |
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages (2024.emnlp-main)
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Holy Lovenia, Rahmad Mahendra, Salsabil Akbar, Lester James Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Moniz, Muhammad Habibi, Frederikus Hudi, Jann Montalan, Ryan Hadiwijaya, Joanito Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Ryanda, Sonny Hermawan, Dan Velasco, Muhammad Kautsar, Willy Hendria, Yasmin Moslem, Noah Flynn, Muhammad Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Aji, Sedrick Keh, Genta Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya
| Challenge: | Southeast Asia (SEA) is home to over 1,300 indigenous languages and 671 million people . prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA . |
| Approach: | They propose to provide a resource center that provides standardized corpora in nearly 1,000 SEA languages across three modalities. |
| Outcome: | a new benchmark assesses the quality of AI models on 36 SEA languages across 13 tasks . the results highlight the importance of SEA as a culturally diverse region . |
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations (2020.coling-main)
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| Challenge: | Prior work identifies a linear gender subspace and removes gender information by eliminating the subspace. |
| Approach: | They propose to use DensRay to obtain interpretable dense subspaces by applying it to attention heads and layers of BERT. |
| Outcome: | The proposed method performs on-par with prior approaches, but is more robust and preserves language model performance better. |
Model Selection for Cross-lingual Transfer (2021.emnlp-main)
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| Challenge: | Existing work has relied on English dev data to select among models that are fine-tuned with different learning rates, number of steps and other hyperparameters, often resulting in suboptimal choices. |
| Approach: | They propose a machine learning approach that uses the fine-tuned model’s internal representations to predict its cross-lingual capabilities. |
| Outcome: | The proposed model selects better than English validation data across twenty five languages, including eight low-resource languages, and often achieves comparable results to model selection using target language development data. |
Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual generalisability of large pre-trained models use English training data and test data in unseen languages. |
| Approach: | They propose to use multilingual pre-trained models to model cross-lingual transfer in a selection of target languages. |
| Outcome: | The proposed model can be used to improve cross-lingual transfer performance in low-resource languages with no labeled training data. |
mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval (2023.emnlp-main)
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| Challenge: | MLIR requires human annotations in multiple languages, making training labor-intensive. |
| Approach: | They propose a multilingual information retrieval model that leverages pre-trained multilingual transformers for dense retrieval. |
| Outcome: | Empirical results show that mAggretriever outperforms state-of-the-art models fine-tuned on English training data. |