File size: 1,710 Bytes
157ac99 d7ae939 157ac99 d7ae939 157ac99 d7ae939 157ac99 9259f7c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
splits:
- 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:
- add |