Evaluation of Sentence Representations in Polish
Table 1: Evaluation of sentence representations on four classification tasks and one semantic relatedness task (SICK-R). For classification, we report accuracy of each model. For semantic relatedness, Pearson correlation between true and predicted relatedness scores is reported. BERT, LASER and USE are multilingual models, other models are Polish only.
['[BOLD] Method', '[BOLD] WCCRS Hotels', '[BOLD] WCCRS Medicine', '[BOLD] SICK-E', '[BOLD] SICK-R', '[BOLD] 8TAGS']
These results were obtained by training a neural network for 10 epochs with a sentence vector as an input, a single hidden layer of 50 units and softmax output layer. The exception was again SICK-R task where the continuous relatedness score was transformed to a probability distribution of relatedness values and the network input was a concatenation of the difference of sentence vectors and their dot product. This approach was compatible with other evaluation studies for SICK-R task in English.