A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts
Table 1: RMSEuser across five traits. Bold highlights best performance.
['Lang.', '[ITALIC] k', 'Model', 'EXT', 'STA', 'AGR', 'CON', 'OPN']
To demonstrate the effectiveness of the proposed model, we evaluate the performance on the user level against models incorporating linguistic and psychologically motivated features. This allows us to directly compare the performance of current state-of-the-art models and C2W2S4PT. (ranked 9th, 6th and 8th in EN, ES and IT). As here, these works predicted scores on text level, and averaged for each user. For each language, we also show the most straightforward baseline Average Baseline which assigns the average of all the scores to each user. and ←hwi∈R256, Weh∈R512×256, bh∈R256, Why∈R256×1, by∈R, dropout rate to the embedding output: 0.5, batch size: 32. Training is performed until 100 epochs are reached.