This repository contains codes for the paper "Disentangled Retrieval and Reasoning for Implicit Question Answering".
python==3.8
torch==1.9.0
nltk==3.6.8
transformers==4.9.0
The weight model named weights.th of baseline should be in the path ./pretrained_model/6_STAR_ORA-p/, which could be downloaded and unzipped from here.
Run the model with default configuration
python main.py
Configuration can be edited in the file main.py or in the running command line, for example,
python main.py \
--num_workers 0 \
--load_pretrained true \
--epoch_num 20 \
--batch_size 16
The json files in the path ./classification/ describes several strategies for the definition and classification of operators, which are crucial components in our reasoning. In the paper, we adopt the 5-class strategy, that is, comparison, logical, entail, numerical and binary. To try another classification strategy, change the configuration --op_classification accordingly.