Repo for EMNLP 2021 paper: Matching-oriented Product Quantization For Ad-hoc Retrieval.
In this work, we identify the limitation of using reconstruction loss minimization in supervised PQ methods, and propose MCL as the new training objective, where the model can be learned to maximize the query-key matching probability to achieve the optimal retrieval accuracy. We further leverage DCS for contrastive sample argumentation, which ensures the effective minimization of MCL.
Use the following command to train on the Mind dataset. And it will automatically select the best model to test.
python run.py
--mode train \
--dataset Mind \
--model_type MoPQ \
--savename MoPQ_Mind \
--cross_device True \
--world_size {the number of your GPUs}
If you use more than one GPU, --cross_device
should be True to activate the Differentiable Cross-device in-batch Sampling.
You can also start the test process manually using following command:
python run.py \
--mode test \
--dataset Mind \
--model_type MoPQ \
--load_ckpt_name ./model/MoPQ_Mind-best.pt
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