Papers by Sneha Kudugunta
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference (2021.findings-emnlp)
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Sneha Kudugunta, Yanping Huang, Ankur Bapna, Maxim Krikun, Dmitry Lepikhin, Minh-Thang Luong, Orhan Firat
| Challenge: | Sparse Mixture-of-Experts (MoE) is a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation. |
| Approach: | They propose to use a task-level routing approach to extract smaller, ready-to-deploy sub-networks from large sparse models by ignoring distillation. |
| Outcome: | Experiments on WMT and a web-scale dataset show that task-level routing outperforms token-level MoE models by +1.0 BLEU on average across 30 language pairs. |
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets (2022.tacl-1)
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Julia Kreutzer, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Ortiz Suarez, Iroro Orife, Kelechi Ogueji, Andre Niyongabo Rubungo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, Mofetoluwa Adeyemi
| Challenge: | Lower-resource corpora have systematic issues, including mislabeled or nonstandard/ambiguous language codes. |
| Approach: | They manually audit the quality of 205 language-specific corpora released with five major public datasets. |
| Outcome: | The results show that lower-resource corpora have systematic issues even for non-proficient speakers. |
MiTTenS: A Dataset for Evaluating Gender Mistranslation (2024.emnlp-main)
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| Challenge: | Existing studies on gender mistranslation in translation systems have highlighted the problem . a dataset of 26 languages is presented to measure the extent of such errors . |
| Approach: | They propose a dataset that measures the extent of gender mistranslation in translation systems . they use handcrafted passages that target known failure patterns and synthetically generated passages . |
| Outcome: | The proposed dataset covers 26 languages from a variety of language families and scripts, including several traditionally under-represented in digital resources. |
Investigating Multilingual NMT Representations at Scale (D19-1)
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| Challenge: | Multilingual Neural Machine Translation models have shown success in transfer learning settings, but their mode of transfer remains elusive. |
| Approach: | They propose to use a representation similarity framework to compare multilingual representations using a SVCCA representation similar to the previous work. |
| Outcome: | The proposed model can be used to compare representations across languages and layers. |
BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer (2024.naacl-long)
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Akari Asai, Sneha Kudugunta, Xinyan Yu, Terra Blevins, Hila Gonen, Machel Reid, Yulia Tsvetkov, Sebastian Ruder, Hannaneh Hajishirzi
| Challenge: | Recent advances in few-shot generalization in natural language processing focus on English. |
| Approach: | They propose a benchmark that unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format and provides a fixed set of few-shot examples and instructions. |
| Outcome: | The proposed framework unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format and provides a fixed set of few-shot examples and instructions. |
MURAL: Multimodal, Multitask Representations Across Languages (2021.findings-emnlp)
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Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen, Sneha Kudugunta, Chao Jia, Yinfei Yang, Jason Baldridge
| Challenge: | Image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. |
| Approach: | They propose a dual encoder that integrates image-text matching and translation pairs to solve two tasks by learning from billions of pairs. |
| Outcome: | The proposed encoder outperforms ALIGN's cross-modal retrieval performance on well-resourced languages and significantly improves on under-resource languages. |
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation (2020.acl-main)
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Aditya Siddhant, Ankur Bapna, Yuan Cao, Orhan Firat, Mia Chen, Sneha Kudugunta, Naveen Arivazhagan, Yonghui Wu
| Challenge: | Existing multilingual NMT approaches do not utilize the abundance of monolingual data, especially in low-resource languages. |
| Approach: | They propose to combine monolingual data with self-supervision to pre-train translation models and fine-tune on small amounts of supervised data. |
| Outcome: | The proposed approach improves translation quality of low-resource languages and zero-shot translation quality. |