README.md

---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: choices
    sequence: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 16078856
    num_examples: 16000
  - name: test
    num_bytes: 1591516
    num_examples: 1600
  download_size: 10736830
  dataset_size: 17670372
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.

## Distractors Generation

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".

## Example

Here you can see the structure of the single sample in the present dataset.


```json
{
  "text": string, # text of the short paragraph
  "choices": list, # list of possible answers, with the correct one plus 4 distractors
  "label": int, # index of the correct anser in the choices
}
```


## Statistics 

Training: 16000

Test: 1600

## Proposed Prompts
Here we will describe the prompt given to the model over which we will compute the perplexity score, as model's answer we will chose the prompt with lower perplexity.
Moreover, for each subtask, we define a description that is prepended to the prompts, needed by the model to understand the task.

Description of the task: 
```txt
Ti verranno poste delle domande, nelle quali è presente un paragrafo, e come possibili risposte varie frasi che possono essere o meno il continuo.\nIndica la frase che rappresenta la continuazione del paragrafo oppure 'nessuna delle precedenti', se nessuna delle continuazioni è corretta."
```

Prompt:
```txt
Paragrafo: '{{text}}'\nDomanda: Quali delle seguenti frasi presenta una continuazione del precedente paragrafo?\nA. '{{choices[0]}}'\nB. '{{choices[1]}}'\nC. '{{choices[2]}}'\nD. '{{choices[3]}}'\nE. {{choices[4]}}\nRisposta:"
```

## Some Results

| DISCOTEX | ACCURACY (2-shots) |
| :-----: | :--: |
| Gemma-2B | 19.18 |
| QWEN2-1.5B | 35.18 |
| Mistral-7B | 56.43 |
| ZEFIRO | 53.68 |
| Llama-3-8B | 58.56 |
| Llama-3-8B-IT | 66.12 |
| ANITA | 66.37 |

## Acknowledge

We want to thanks the authors of this resource to publicly release such interesting benchmark.

Further, We want to thanks the student of [MNLP-2024 course](https://naviglinlp.blogspot.com/), where with their first homework tried different interesting prompting strategies.

The data can be freely downloaded form this [link](https://github.com/davidecolla/DisCoTex/tree/master/data).

## License
The original data come under license [CC-BY-NA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)