Akirato / KDD-Cup-2022-Amazon

This repository is the team ETS-Lab's solution towards KDD Cup 2022.

KDD-Cup-2022-Amazon

This repository is the team ETS-Lab's solution for Amazon KDD Cup 2022. You can find our code submission here or check the solution paper here.

General solution

• We trained 6 cross encoder models for each language which differs in the pertained models, training method (e.g., knowledge distillation), and data splitting. In total, six identical models (2 folds x 3 models) are used to produce the initial prediction (4 class probability) of the query-product pair. In use those models only, the public set score for task 2 is around 0.816.

• For Task 1, we used the output 4 class probability with some simple features to train a lightgbm model, calculate the expected gain ($P_e1 + P_s0.1 + P_c*0.01$), and sort the query-product list by this gain.

• For task 2 and Task 3, we used lightgbm to fuse those prediction with some important features. Most important features are designed based on the potential data leakage from task 1 and the behavior of the query-product group:

  • The stats (min, medium and max) of the cross encoder output probability grouped by query_id (0.007+ in Task 2 Public Leaderboard)
  • The percentage of product_id in Task 1 product list grouped by query_id (0.006+ in Task 2 Public Leaderboard)

Small modification towards Cross Encoder architecture

• As the product context has multiple fields (title, brand, and so on), we use neither the cls token nor mean (max) pooling to get the latent vector of the query-product pair. Instead, we concatenate the hidden states of a predefined token (query, title, brand color, etc.). The format is:

  [CLS] [QUERY] <query_content> [SEP] [TITLE] <title_content> [SEP] [Brand] <brand_content> [SEP] ...
  

  where [TEXT] is the special token and <text_content> is the text contents.

Code submission speed up

1. Pre-process product token and saved as a HDF5 file.
2. Transfer all model to ONNX with FP16 precision.
3. Pre-sorted the product id to reduce the side impact of batch zero padding.
4. Use relative small mini-batch size when inference.

How to run code

• You need to write down some config.yaml file

• For training:

  python train.py -c config/us-bigbird-kd-0.yaml

• For inference:

  python inference.py -c config/us-bigbird-kd-0.yaml -w last -ds test