charagram: Embedding Words and Sentences via Character n-grams
Table 3: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface (omitting the official task score columns).
['Dataset', '50%', '75%', 'Max', 'charCNN', 'charLSTM', 'paragram-phrase', 'charagram-phrase']
For comparison, we report performance for the median (50%), third quartile (75%), and top-performing (Max) systems from the shared tasks. We observe strong performance for the charagram-phrase model. It always does better than the charCNN and charLSTM models, and outperforms the paragram-phrase model on 15 of the 22 tasks. Furthermore, charagram-phrase matches or exceeds the top-performing task-tuned systems on 5 tasks, and is within 0.003 on 2 more. The charLSTM and charCNN models are significantly worse, with the charCNN being the better of the two and beating paragram-phrase on 4 of the tasks.