Rational Recurrences
Table 5: Text classification test accuracy averaged over 5 runs. ± denotes standard deviation, and bold font indicates best averaged performance.
['[BOLD] Model', '[BOLD] Amazon', '[BOLD] SST', '[BOLD] subj', '[BOLD] CR']
We report the average performance of 5 trials different only in random seeds. rrnn(F) outperforms all other models on 3 out of the 4 datasets. For Amazon, the largest one, we do not observe significant differences between rrnn(F) and rrnn(C), while both outperform others. This may suggest that the interpolation of unigram and bigram features by rrnn(F) is especially useful in small data setups. As in the language modeling experiments, rrnn(B)m+ underperforms all other models in most cases, and in particular rrnn(B). These results provide evidence that replacing the real semiring in rational models might be challenging. We leave further exploration to future work.