Papers by Anna Sun
Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation (2023.findings-acl)
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| Challenge: | Sparsely gated Mixture of Experts (MoE) models are a compute-efficient method to scale model capacity for multilingual machine translation tasks. |
| Approach: | They propose a regularization strategy that prevents over-fitting of MoE models on low-resource tasks and conditional MoE Routing and curriculum learning methods that prevent over- fitting. |
| Outcome: | The proposed methods improve the performance of MoE models on low-resource tasks without adversely affecting high-res tasks. |
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks (2023.acl-long)
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| Challenge: | Neural based end-to-end frameworks have achieved remarkable success in speech-totext tasks, such as automatic speech recognition (ASR) and speech- totext translation (ST). |
| Approach: | They propose to combine Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks and leverage AED's strength in non-monotonic sequence to sequence learning while retaining Transducers streaming property. |
| Outcome: | The proposed model outperforms Transducer and Attention based Encoder-Decoder (TAED) on the MuST-C dataset and shows that it is not bound by any specific language model. |
stopes - Modular Machine Translation Pipelines (2022.emnlp-demos)
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Pierre Andrews, Guillaume Wenzek, Kevin Heffernan, Onur Çelebi, Anna Sun, Ammar Kamran, Yingzhe Guo, Alexandre Mourachko, Holger Schwenk, Angela Fan
| Challenge: | Neural machine translation is a natural language deep learning application that needs data to be trained. |
| Approach: | They describe a framework that empowers scalability and versatility for research use cases. |
| Outcome: | The proposed framework empowers scalability and versatility for research use cases. |
Efficiently Upgrading Multilingual Machine Translation Models to Support More Languages (2023.eacl-main)
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| Challenge: | Existing multilingual machine translation models need to be upgraded as data becomes available in more languages. |
| Approach: | They propose three techniques that speed up the effective learning of new languages and alleviate catastrophic forgetting . |
| Outcome: | The proposed techniques exceed the performance of a same-sized baseline model with 30% computation and recover the performance a larger model trained from scratch with over 50% reduction in computation. |