ColBERT-X for English-Chinese CLIR using Translate-Distill
CLIR Model Setting
- Query language: English
- Query length: 32 token max
- Document language: Chinese
- Document length: 180 token max (please use MaxP to aggregate the passage score if needed)
Model Description
Translate-Distill is a training technique that produces state-of-the-art CLIR dense retrieval model through translation and distillation.
plaidx-large-zho-tdist-t53b-engeng is trained with KL-Divergence from the t53b MonoT5 reranker inferenced on
English MS MARCO training queries and English passages.
Teacher Models:
Training Parameters
- learning rate: 5e-6
- update steps: 200,000
- nway (number of passages per query): 6 (randomly selected from 50)
- per device batch size (number of query-passage set): 8
- training GPU: 8 NVIDIA V100 with 32 GB memory
Usage
To properly load ColBERT-X models from Huggingface Hub, please use the following version of PLAID-X.
pip install PLAID-X==0.3.1
Following code snippet loads the model through Huggingface API.
from colbert.modeling.checkpoint import Checkpoint
from colbert.infra import ColBERTConfig
Checkpoint('hltcoe/plaidx-large-zho-tdist-t53b-engeng', colbert_config=ColBERTConfig())
For full tutorial, please refer to the PLAID-X Jupyter Notebook, which is part of the SIGIR 2023 CLIR Tutorial.
BibTeX entry and Citation Info
Please cite the following two papers if you use the model.
@inproceedings{colbert-x,
author = {Suraj Nair and Eugene Yang and Dawn Lawrie and Kevin Duh and Paul McNamee and Kenton Murray and James Mayfield and Douglas W. Oard},
title = {Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models},
booktitle = {Proceedings of the 44th European Conference on Information Retrieval (ECIR)},
year = {2022},
url = {https://arxiv.org/abs/2201.08471}
}
@inproceedings{translate-distill,
author = {Eugene Yang and Dawn Lawrie and James Mayfield and Douglas W. Oard and Scott Miller},
title = {Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation},
booktitle = {Proceedings of the 46th European Conference on Information Retrieval (ECIR)},
year = {2024},
url = {https://arxiv.org/abs/2401.04810}
}