This repo contains a collection of Kaggle notebooks that were used to train the models in our paper.

• train and test contains skeleton code for training and testing the NLI models, which were adapted to suit different models and datasets for our experiments
• bm25-crossencoder contains the code used to generate the top-100 snippets for each claim, and the top-5 reranking using a cross encoder. The dataset is available at https://www.kaggle.com/datasets/askuiper/quantemp, which also includes the QuanTemp dataset itself, and the corpus.
• claimdecomp contains code used to train the BART model to decompose the claims in QuanTemp, and the reranking code to extract the top-5 snippets from the bm25 top-100. The dataset is available at https://www.kaggle.com/datasets/askuiper/quantemp-decomp-data.
• temporal-reranking contains the code used to rerank the top-5 snippets from the bm25 top-100 by taking into account temporal information. The dataset is available at https://www.kaggle.com/datasets/lucasvm/quantemp-temporal-rerank.
• strategyqa contains the code used to invistigate the use of a model trained on StrategyQA in order to decompose questions and answer them iteratively using a Deberta model trained on SQuAD. The dataset is available at https://www.kaggle.com/datasets/lucasvm/strategyqa-decomp-quantemp.