---
dataset_info:
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  - name: id
    dtype: int64
  - name: text
    dtype: string
  - name: choices
    sequence: string
  - name: label
    dtype: int64
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  - name: train
    num_bytes: 16206856
    num_examples: 16000
  - name: test
    num_bytes: 1604316
    num_examples: 1600
  download_size: 10835222
  dataset_size: 17811172
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

Assessing DIScourse COherence in Italian TEXts (DISCOTEX)
Original Paper: https://sites.google.com/view/discotex/

Task presented at EVALITA-2023

The original task is about modelling discourse coherence for Italian texts.

We focalized only on the first sub-task: Last sentence Classification: given a short paragraph, and an individual sentence (target), the model will be asked to classify whether the target follows or not the paragraph.

To assess the capability of a Language Model to solve such kind of task we reframed the task as multi-choice QA.

The question will ask to the model given a short paragraph which target sentence is the correct between a list of four, the answers will be the starting letters of the relative target, and a fifth option that indicate that no one target is the correct continuation.

For each sample, if the sample has 1 as label, we set the relative target as gold answer and three other random targets (from other samples) as distractors. On the other way around, if the sample has 0 as label, we set the relative target and other three random targets (from other samples) as distractors, as the gold answer will be chosen the sentence: "nessuna delle precedenti".

Data statistics:
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