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ryokamoi / wice

This repository contains the dataset and code for "WiCE: Real-World Entailment for Claims in Wikipedia" in EMNLP 2023.

Home Page:https://arxiv.org/abs/2303.01432

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finetune T5 model on WICE's subclaim data

qjh-nj opened this issue · comments

commented

Hello!
I have some questions about the process of finetuning T5 on subclaim data:

  1. train on the three-way classification
  2. use the MAX entailment strategy to get the classification probabilities of the subclaim data such as "test00561-0", "test00561-1".....
  3. use the harmonic mean to calculate the classification probabilities of the claim data such as "test00561"( or if the results of Tabel 5 does not calculate the classification probabilities of the claim data? Just 1, 2 step? )
  4. the results of Tabel 5 : if the classification probabilities of T5 > 0.5 then we classify it as "e"( supported claim ) ?

Because my results on subclaim are lower than that in paper(85.1, 82.7 Table 5), I want to know if the choices above are correct?
Thanks!