EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
Authors: Yuqiao Wen, Behzad Shayegh, Chenyang Huang, Yanshuai Cao, Lili Mou
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments on IWSLT (Cettolo et al. 2017) and Europarl (Koehn 2005), two popular multilingual translation datasets for zero-shot machine translation. Results show that EBBS can generate high-quality translations and outperform existing ensemble techniques. |
| Researcher Affiliation | Collaboration | Yuqiao Wen1,*, Behzad Shayegh1, , Chenyang Huang1, Yanshuai Cao2, Lili Mou1,3 1Dept. Computing Science, Alberta Machine Intelligence Institute (Amii), University of Alberta 2RBC Borealis 3Canada CIFAR AI Chair, Amii EMAIL, EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | Yes | We provide the detailed pseudocode for EBBS in Algorithm 1 and an illustration in Figure 1. |
| Open Source Code | Yes | Git Hub https://github.com/MANGA-UOFA/EBBS |
| Open Datasets | Yes | We evaluated EBBS on two popular benchmark datasets for zero-shot machine translation: IWSLT (Cettolo et al. 2017), which contains 4 languages (with English) and 6 zero-shot directions; and Europarl v7 (Koehn 2005), which contains 9 languages and 56 zero-shot directions. |
| Dataset Splits | No | The paper mentions using IWSLT and Europarl datasets and refers to replicating a previous model's training setup (Liu et al. 2021) and standard practice for selecting subsets for distillation (Fan et al. 2021). However, it does not explicitly state the train/validation/test split percentages or sample counts used in this paper. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of a Transformer architecture and a byte pair encoding tokenizer, but it does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Specifically, the neural architecture in (Liu et al. 2021) is a 5-layer encoder decoder Transformer for IWSLT, but has 8 layers for Europarl to accommodate more training data and languages. For EBBS, we used a beam size of five for both upper- and lower-level beams. In our experiment, we implemented standard beam search for comparison, where we also used a beam size of five, following the common practice (Meister, Cotterell, and Vieira 2020). |