End-to-end Learning for Short Text Expansion
Table 3. Classification performance of compared methods
['Settings', 'Methods', 'Wikipedia Micro-F1', 'Wikipedia Macro-F1', 'Dblp Micro-F1', 'Dblp Macro-F1', 'Twitter Micro-F1', 'Twitter Macro-F1']
We compare three different types of approaches: methods with only short text features, short text expansion using classical query expansion methods, and end-to-end short text expansion methods based on memory networks. For the latter two types of methods, besides leveraging external long documents as memory, we also investigate the effect of treating the training short documents as memory. For all the methods, the performance are averaged over five runs with random parameter initialization. In general, our methods significantly outperforms the original memory networks and baseline methods only using original features of short texts and classical query expansion methods.