Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sparse MoE with Language Guided Routing for Multilingual Machine Translation
Authors: Xinyu Zhao, Xuxi Chen, Yu Cheng, Tianlong Chen
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Sufficient experimental studies on MMT benchmarks with {16, 50, 100} languages and various network architectures, consistently validate the superior performance of our proposals. |
| Researcher Affiliation | Academia | 1The University of North Carolina at Chapel Hill 2The University of Texas at Austin 3The Chinese University of Hong Kong 4MIT 5Harvard University |
| Pseudocode | Yes | Algorithm 1 DEA in our proposed Lingual-SMo E. |
| Open Source Code | Yes | 1Our code is provided at https://github.com/UNITES-Lab/Lingual-SMo E. |
| Open Datasets | Yes | We evaluate the proposed Lingual-SMo E on the representative multilingual neural machine translation dataset, i.e., OPUS-100 (Zhang et al., 2020) that contains 100 languages and 94 validation and test language pairs. |
| Dataset Splits | Yes | We evaluate the proposed Lingual-SMo E on the representative multilingual neural machine translation dataset, i.e., OPUS-100 (Zhang et al., 2020) that contains 100 languages and 94 validation and test language pairs. (...) We split the 94 validation language pairs in OPUS-100 into three groups based on their training data size: high-resource (> 0.9M, 45 languages), low-resource (< 0.1M, 26 languages), and medium-resource (other, 28 languages) (Zhang et al., 2020). (...) Table A6: The statistics of the OPUS-100 datasets and its sub-datasets. Datasets ... Train Validation Test |
| Hardware Specification | Yes | Experiments are conducted using Fairseq (Ott et al., 2019) with 8 RTX A6000 GPUs. |
| Software Dependencies | Yes | BLEU Signature: nrefs:1 | case:mixed | eff:no | tok:13a | smooth:exp | version:2.3.1 |
| Experiment Setup | Yes | The training processes have 35K, 100K, and 200K iterations for OPUS-16, OPUS-50, and OPUS-100, respectively. With a learning rate of 5e-4, we optimize models with Adam using (β1, β2, ϵ) = (0.9, 0.98, 10e-8) (Kingma & Ba, 2015). The learning rate schedule follows the Inverse Square Root with a specific number of warm-up steps set to 4,000. A temperature-based data sampling strategy is utilized to train our models (Aharoni et al., 2019). The temperature is set to 1.5 for OPUS-16, and 5 for OPUS-50 and OPUS-100. The dynamic expert allocation uses a value of n equal to 5,000 iterations for experiments on OPUS-16, OPUS-50, and 10,000 iterations for OPUS-100. In addition, the ratio of expert number exploring updates is set to 0.8, and the threshold controlling expert capacity number λ is 0.1 for OPUS-16, OPUS-50 and 0.01 for OPUS-100. |