A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts
Table 2: RMSEtweet across five traits level. Bold highlights best performance.
['Lang.', 'Model', 'EXT', 'STA', 'AGR', 'CON', 'OPN']
Additionally, two simpler RNN-based models, namely Bi-GRU-Char and Bi-GRU-Word, which only work on character and word level respectively but share the same structure of the final MLP classifier (hs and ^ys), have also been presented in contrast to the more sophisticated character to word compositional model C2W2S4PT. For Bi-GRU-Char and Bi-GRU-Word, we set the character and word embedding size to 50 and 256 respectively. Due to time constraints, we did not perform hyper-parameter fine-tuning for the RNN-based models and C2W2S4PT.