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--- |
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dataset_info: |
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features: |
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- name: id |
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dtype: int64 |
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- name: text |
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dtype: string |
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- name: target |
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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: 9230770 |
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num_examples: 16000 |
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- name: test |
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num_bytes: 917067 |
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num_examples: 1600 |
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download_size: 6673902 |
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dataset_size: 10147837 |
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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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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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Data statistics: |
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- add |
