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--- |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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- name: choices |
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sequence: string |
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- name: label |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 16078856 |
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num_examples: 16000 |
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- name: test |
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num_bytes: 1591516 |
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num_examples: 1600 |
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download_size: 10736830 |
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dataset_size: 17670372 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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--- |
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# Assessing DIScourse COherence in Italian TEXts (DISCOTEX) |
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Original Paper: https://sites.google.com/view/discotex/ |
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Task presented at EVALITA-2023 |
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The original task is about modelling discourse coherence for Italian texts. |
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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. |
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To assess the capability of a Language Model to solve such kind of task we reframed the task as **Multi-Choice QA**. |
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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. |
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## Distractors Generation |
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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". |
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## Example |
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Here you can see the structure of the single sample in the present dataset. |
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```json |
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{ |
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"text": string, # text of the short paragraph |
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"choices": list, # list of possible answers, with the correct one plus 4 distractors |
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"label": int, # index of the correct anser in the choices |
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} |
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``` |
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## Statistics |
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Training: 16000 |
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Test: 1600 |
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## Proposed Prompts |
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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. |
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Moreover, for each subtask, we define a description that is prepended to the prompts, needed by the model to understand the task. |
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Description of the task: |
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```txt |
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Ti verranno poste delle domande nelle quali è presente un paragrafo, e come possibili risposte varie frasi che possono essere o meno la continuazione del paragrafo.\nIndica la frase che rappresenta la continuazione più probabile del paragrafo, oppure \"nessuna delle precedenti\" se nessuna delle continuazioni è corretta.\n\n |
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``` |
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Prompt: |
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```txt |
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Paragrafo: \"{{text}}\"\nDomanda: Quali delle seguenti frasi è la continuazione più probabile del precedente paragrafo?\nA. \"{{choices[0]}}\"\nB. \"{{choices[1]}}\"\nC. \"{{choices[2]}}\"\nD. \"{{choices[3]}}\"\nE. {{choices[4]}}\nRisposta: |
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``` |
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## Results |
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| DISCOTEX | ACCURACY (2-shots) | |
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| :-----: | :--: | |
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| Gemma-2B | 19.18 | |
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| QWEN2-1.5B | 35.18 | |
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| Mistral-7B | 56.43 | |
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| ZEFIRO | 53.68 | |
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| Llama-3-8B | 58.56 | |
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| Llama-3-8B-IT | 66.12 | |
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| ANITA | 66.37 | |
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## Acknowledge |
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We would like to thank the authors of this resource for publicly releasing such an intriguing benchmark. |
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Additionally, we extend our gratitude to the students of the [MNLP-2024 course](https://naviglinlp.blogspot.com/), whose first homework explored various interesting prompting strategies. |
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The original dataset is freely available for download [link](https://github.com/davidecolla/DisCoTex/tree/master/data). |
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## License |
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The original data come under license [CC-BY-NA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) |
