Colorless green recurrent networks dream hierarchically
Table 1: Experimental results for all languages averaged across the five best models in terms of perplexity on the validation set. Original/Nonce rows report percentage accuracy, and the numbers in small print represent standard deviation within the five best models.
['[EMPTY]', 'It', 'En', 'He', 'Ru']
(this is required by the evaluation procedure outlined below). The average number of context words separating the cue and the target ranged from 3.6 (Hebrew) to 4.5 (Italian). We report results averaged across the five models with the lowest validation perplexity, as well as standard deviations across these models. In summary, the LSTM clearly outperformed the other LMs. Rather surprisingly, its performance on nonce sentences was only moderately lower than on original ones; in Italian this gap was only 6.6%.