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Think about it! Improving defeasible reasoning by first modeling the question scenario

Code and data Think about it! Improving defeasible reasoning by first modeling the question scenario (EMNLP 2021)

Setting up

• Download the pre-trained models from here (or if you use gdown, you can run gdown --id 1QKSnMLpt0TfM-Jxu-eI-c92qHSjcIAov to directly download the models zip (23GB)).

• Download the data directory from here and unzip in the the root folder (or run gdown --id 1iexS3RrtSl3T2B2fGDCz9m0nVotula8x).

Use cases

1. Inference

• Use scripts/table5.sh to run inference for all models/dataset. This will recreate the numbers presented in Table 5 in the paper.

• Each output file contains per-sample inferred and true labels, as well as the MOE gate values if applicable.

2. Training

• You can run training using scripts/train.sh script.

Usage:

scripts/train.sh MODEL_TYPE DATA_DIR GRAPH_NAME GPU

where: - MODEL_TYPE: one of str, moe, gcn, gcn_moe - DATA_DIR: path to the directory containing the dataset. - GRAPH_NAME: name of the graph to be used for training. - GPU: GPU to use. If not specified, will use the first available GPU.

• For example, to train an moe model on atomic dataset with cleaned graph, run:

    scripts/train.sh moe data/defeasible_graph_augmented_qa/t5/atomic/influence_graphs_cleaned.jsonl 0

• Sample unit test data is located in data/unit_test. The following command runs a unit test:

bash scripts/train.sh moe data/unit_test/ influence_graphs.jsonl 0

Data and pre-trained models

• Data documentation.

• Pre-trained models documentation