The baseline for both tasks will be computed by employing the one-hot vectors representation:

• For Sub-task 1: the vector will be extracted from each sentence si in the input prompt P = {s1,s2, ..., sn} and another vector will be created for the target sentence t. The distance between P and the target sentence t, D(P, t) will be computed as the average distance between each pair involving one item si and t based on a distance metric Dist (e.g., Hamming distance, Jaccard, or a Edit distance):

To decide whether the target sentence t is coherent with the paragraph P we will first compute the median value across the whole training dataset, and then we will use this as a threshold: all the occurrences with a value above the median will be considered coherent, incoherent otherwise.

• For Sub-task 2: the vector will be extracted from each sentence si in the input prompt P = {s1,s2, ..., sn}; that is the following vectors set will be computed:

The proximity between each two vectors ⟨vx, vx+1⟩ ∈ V will then be computed through a distance metric Dist(s1,s2) (e.g. Jaccard), thereby resulting in (n − 1) distance scores, grasping the degree of semantic overlap between each two neighbouring sentences. In order to compute the coherence score for the paragraph P score(P), we will average the scores featuring each pair of adjacent sentences. The value will then be compared with the human rating with correlation indices:

where corr indicates the Pearson or Spearman correlation index.