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..
Robust Optimization for Multilingual Translation with Imbalanced Data
Authors: Xian Li, Hongyu Gong
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ran experiments on common benchmarks (TED, WMT and OPUS-100) with varying degrees of data imbalance. CATS effectively improved multilingual optimization and as a result demonstrated consistent gains on low resources (+0.8 to +2.2 BLEU) without hurting high resources. |
| Researcher Affiliation | Industry | Facebook AI EMAIL |
| Pseudocode | Yes | Algorithm 1 Curvature Aware Task Scaling (CATS). |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its own source code, nor does it include a link to a code repository for its methodology. |
| Open Datasets | Yes | Datasets. We experiment on three public benchmarks of multilingual machine translation with varying characteristics of imbalanced data as is shown in Table 1. ...TED [53] WMT[34] OPUS-100[62]... |
| Dataset Splits | Yes | We choose the best checkpoint by validation perplexity and only use the single best model without ensembling. We use the same preprocessed data by the Multi DDS baseline authors [53], and followed the same procedure to preprocess OPUS-100 data released by the baseline [62]. |
| Hardware Specification | No | The paper mentions training 'with the same compute budget' but does not provide specific hardware details such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions using the 'Transformer architecture' and cites 'fairseq' as a toolkit, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We provide detailed training hyperparameters in Appendix B. |