Evaluation of Sentence Representations in Polish
Table 2: Evaluation of aggregation techniques for word based sentence representations with fixed vector dimensionality (300). Baseline model uses simple averaging, SIF is a method proposed by arora2017a, Max Pooling is a concatenation of arithmetic mean and max pooled vector from word embeddings.
['[BOLD] Method', '[BOLD] WCCRS Hotels', '[BOLD] WCCRS Medicine', '[BOLD] SICK-E', '[BOLD] SICK-R', '[BOLD] 8TAGS']
Baseline is the same as in the previous experiment. We can see that the effect of applying SIF method is not clearly beneficial. The performance is similar to the baseline model with small gains for some tasks and small losses for others. The authors of SIF demonstrated that the method might improve semantic similarity of word vectors but it seems that there are no improvements for other tasks, at least in Polish. Max pooling, however, turned out to beneficial for some problems. For each of the evaluated models, concatenating max pooled vector resulted in performance comparable to the baselines on classification tasks and significant improvements for tasks based on SICK dataset, with gains of 3% to 5%.