Meta-learning of textual representationsThis work was partially supported by CONACyT under grant Aprender Objetos de Internet para Buscarlos con un Robot, ID: CB-250938.
Table 2: Average accuracy [0,1] and average rank [60,1] of different strategies in 81 corpus, the last row indicates the number of times the best representation was predicted. (1) Nearest corpus, (2) classification, (3) performance regression, (4) rank prediction.
['Method', 'Best', '(1)', '(2)', '(3)', '(4)', 'Random']
We evaluated the 4 meta-learning strategies with unseen tasks following a leave-one-out setting, using the results from 60 representations in the rest of the tasks as knowledge to decide which representation to recommend. The objective for the strategies, then, is to select what in exhaustive search was found to be the best representation. We compared the average performance achieved by our strategies in 5 runs against the best solution found and the average performance of all of the considered representations.